Plasma cells serve a crucial role in the human immune system and are important in tumor progression. However, the specific role of plasma cell immune-related genes (PCIGs) in tumor progression remains unclear. Therefore, the present study aimed to establish a risk assessment model for patients with lung adenocarcinoma (LUAD) based on PCIGs. The data used in the present study were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. After identifying nine PCIGs, a risk assessment model was constructed and a nomogram was developed for predicting patient prognosis. To explore the molecular mechanism and clinical significance, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis and drug sensitivity prediction were performed. Furthermore, the accuracy of the model was validated using reverse transcription-quantitative PCR (RT-qPCR). The present study constructed a risk assessment model consisting of nine PCIGs. Kaplan-Meier survival curves indicated a worse prognosis in the high-risk subgroup (risk score ≥0.982) compared with that in the low-risk subgroup. The nomogram exhibited predictive value for survival prediction (area under the curve=0.727). GSEA enrichment analysis revealed enrichment of the focal adhesion and extracellular matrix-receptor interaction pathways in the high-risk group. Moreover, the high-risk group exhibited a higher TMB, as demonstrated by the TME analysis showing lower ESTIMATE scores. Drug sensitivity prediction facilitated potential drug selection. Subsequently, differential gene expression was validated in multiple LUAD cell lines using RT-qPCR. In conclusion, the risk assessment model based on nine PCIGs may be used to predict the prognosis and drug selection in patients with LUAD.

Lung adenocarcinoma is a deadly malignancy with immune evasion playing a key role in tumor progression. Glucose metabolism is crucial for T cell function, and the nucleolar protein NCL may influence T cell glucose metabolism. This study aims to investigate NCL's role in T cell glucose metabolism and immune evasion by lung adenocarcinoma cells.

Utilizing single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we analyzed cell clustering, annotation, and prognosis. In vitro experiments involved manipulating NCL expression in CD8+ T cells to study immune function and glucose metabolism. In vivo studies using an orthotopic transplant mouse model monitored NCL's impact on CD8+ T cell glucose metabolism and anti-tumor immune function.

NCL was associated with T cell dysfunction and glucose metabolism. NCL silencing enhanced CD8+ T cell glucose metabolism, cytotoxicity, and infiltration, while NCL overexpression had the opposite effect. NCL overexpression relieved MYC-mediated transcriptional repression of TXNIP, reducing CD8+ T cell glucose metabolism. In vivo, NCL inhibited CD8+ T cell glucose metabolism through the MYC/TXNIP axis, hindering anti-tumor immune function.

NCL overexpression suppresses CD8+ T cell glucose metabolism and anti-tumor immune function, promoting lung adenocarcinoma progression via the MYC/TXNIP axis.

Atrial fibrillation (AF) is a significant precursor to cerebral embolism. Our study sought to unearth new diagnostic biomarkers for atrial fibrillation-related cerebral embolism (AF-CE) by meticulously examining multiple GEO datasets and meta-analysis. The gene expression omnibus (GEO) database provided RNA sequencing data associated with AF and stroke. We began by pinpointing genes with varied expressions in AF-CE patient blood samples. A meta-analysis was subsequently undertaken using several RNA sequencing datasets to verify these genes. LASSO regression discerned key genes for AF-CE, with their diagnostic prowess verified through ROC curve examination. Active signaling pathways within stroke patients were discerned via GO and KEGG enrichment, with PPI interactions detailing gene interplay. Differential gene analysis revealed an upregulation of sixteen genes and a downregulation of four in stroke patient blood samples. Eight genes showcased varied expression in the meta-analysis. LASSO regression zeroed in on five of these, culminating in HIST1H2BH's identification as a characteristic gene. HIST1H2BH's prowess in predicting AF-CE was confirmed through ROC. Integrin signaling, platelet activation, ECM interactions, and the PI3K-Akt pathway were found active in stroke victims. HIST1H2BH's interaction with the notably upregulated ITGA2B was spotlighted by PPI. Additionally, HIST1H2BH exhibited links with NK cells and eosinophils. HIST1H2BH emerges as an insightful diagnostic beacon for AF-CE. Its presence, post AF, potentially modulates pathways, accentuating platelet activation and consequent thrombus generation, leading to cerebral embolism.

Autoimmune diseases (AIDs) are immune system disorders where the body exhibits an immune response to its own antigens, causing damage to its own tissues and organs. The pathogenesis of AIDs is incompletely understood. However, recent advances in immune repertoire sequencing (IR-seq) technology have opened-up a new avenue to study the IR. These studies have revealed the prevalence in IR alterations, potentially inducing AIDs by disrupting immune tolerance and thereby contributing to our comprehension of AIDs. IR-seq harbors significant potential for the clinical diagnosis, personalized treatment, and prognosis of AIDs. This article reviews the application and progress of IR-seq in diseases, such as multiple sclerosis, systemic lupus erythematosus, rheumatoid arthritis, and type 1 diabetes, to enhance our understanding of the pathogenesis of AIDs and offer valuable references for the diagnosis and treatment of AIDs.

Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associated signature in melanoma, which had never been elucidated yet.

A new Treg signature, Treg.Sig, was created using a computational framework guided by machine learning, utilizing transcriptome data from both single-cell RNA-sequencing (scRNA-seq) and bulk RNA-sequencing (bulk-seq). Among the 10 Treg.Sig genes, hub gene STAT1's function was further validated in ICI resistance in melanoma mice receiving anti-PD-1 treatment.

Treg.Sig, based on machine learning, was able to forecast survival outcomes for melanoma across training dataset and external test dataset, and more importantly, showed superior predictive power than 51 previously established signatures. Analysis of the immune profile revealed that groups with high Treg.Sig levels exhibited immune-suppressive conditions, with inverse correlations observed between Treg.Sig and anti-cancer immune responses. Notably, among the 10 Treg.Sig genes, hub gene STAT1 mutation harbored lower response rate in ICIs-treated cohort. Mechanistically, STAT1 impinged on ICI resistances by modulating the phenotypic switch in N2 neutrophil polarization in melanoma mice receiving anti-PD-1 therapy, which affects overall survival.

The study developed a promising Treg.Sig signature that predicts ICI response of melanomas and could be used for selecting patients for immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting Treg-associated genes.

CD4+ T cells play an indispensable role in anti-tumor immunity and shaping tumor development. We sought to explore the characteristics of CD4+ T cell marker genes and construct a CD4+ T cell-related prognostic signature for stage III-IV colorectal cancer (CRC) patients.

We combined scRNA and bulk-RNA sequencing to analyze stage III-IV CRC patients and identified the CD4+ T cell marker genes. Unsupervised cluster analysis was performed to divide patients into two clusters. The LASSO and multivariate Cox regression were performed to establish a prognostic-related signature. RT-qpcr and immunofluorescence staining were performed to examine the expression of ANXA2 in CRC tissue.

We found a higher infiltration abundance of activated memory CD4+ T cells was associated with improved prognosis in stage III-IV CRC patients. Patients were divided into two subgroups with distinct clinical and immunological behaviors based on CD4+ T cell marker genes. And then a prognostic signature consisting of six CD4+ T cell marker genes was established, which stratified patients into high- and low-risk groups. Immune spectrum showed that the low-risk group had higher immune cell infiltration than the high-risk group. Furthermore, the risk score of this signature could predict the susceptibility of stage III-IV CRC patients to immune checkpoint inhibitors and chemotherapy drugs. Finally, we validated that ANXA2 was enriched in Tregs and was associated with infiltration of Tregs in CRC tumor microenvironment.

The CD4+ T cell-related prognostic signature established in the study can predict the prognosis and the response to immunotherapy in stage III-IV CRC patients. Our findings provide new insights for tumor immunotherapy of advanced CRC patients.

Regulatory Factor X (RFX) transcription factors have been implicated in different cancers. Ras association domain family (RASSF) has been shown clinical significance in lung cancer. This paper was to investigate the interaction of RFX2 and RASSF1 in lung adenocarcinoma (LUAD).

The transcriptome differences of LUAD patients in GSE32863, GSE43458, and GSE21933 datasets were analyzed. A-549 and NCI-H358 cell lines after overexpression of RFX2 were co-cultured with activated CD8+ T cells, and the release of IFN-γ, GZMB, PRF1 by CD8+ T cells, and PD-L1 in the LUAD cells were detected. Cell viability, invasion, and apoptosis were analyzed by CCK-8, Transwell, and TUNEL assays. Dual-luciferase assay and ChIP were conducted to detect the interaction between RFX2 and RASSF1 promoter. An in vivo tumor model was constructed to monitor tumor growth. YAP protein levels and phosphorylation were measured. A-549 and NCI-H358 cells treated with DMSO or PY-60 after RFX2 overexpression were co-cultured with activated CD8+ T cells.

RFX2 was notably downregulated in LUAD. RFX2 overexpression increased infiltrating CD8+ T cells within transplanted tumors and inhibited immune escape, proliferation, and invasion of LUAD cells. RFX2 was enriched in the RASSF1 promoter, and RFX2 activated RASSF1 transcription by binding to the RASSF1 promoter. RASSF1 knockdown reversed the ability of RFX2 overexpression to inhibit immune escape. RFX2 depletion downregulated RASSF1, which reduced YAP phosphorylation, thus affecting the Hippo pathway to promote the immune escape.

RFX2 Loss in LUAD downregulates RASSF1 expression and YAP phosphorylation, thereby promoting immune escape through Hippo signaling.

Tumor-associated macrophages (TAMs) play pivotal roles in innate immunity and contribute to the advancement of lung cancer. We aimed to identify novel TAM-related biomarkers and significance of macrophage infiltration in lung adenocarcinoma (LUAD) through an integrative analysis of single-cell RNA-sequencing (scRNA-seq) data. To describe the cell atlas and construct a novel prognostic signature in LUAD.

The gene signature linked to TAMs was identified utilizing Scanpy from the scRNA-seq dataset GSE131907. Subsequent analysis involved evaluating the expression levels of these genes, their potential molecular mechanisms, and prognostic significance in LUAD using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We also constructed a risk score models through LASSO Cox regression for these genes. The underlying mechanism was further elucidated through the application of GSEA, ESTIMATE, TIDE, and other bioinformatic algorithms.

Single-cell atlas was described by analyze 29 scRNA-seq samples from 19 LUAD patients. The TAMs-related gene signature (TGS) was identified as an independent prognostic factor by LASSO Cox regression analysis using differential expression genes (DEGs) derived from pro- and anti-inflammatory macrophage cells. Risk score model including nine TAMs-related genes (FOSL1, ZNF697, ADM, UBE2S, TICAM1, S100P, BIRC3, TLE1, and DEFB1) were obtained for prognosis construction. Moreover, the risk model underwent additional validation in four external GEO cohorts: GSE31210, GSE72094, GSE26939, and GSE30219. Interestingly, TGS-high tumors revealed enrichments in TGF-β signaling and hypoxia pathways, which shown low immune infiltration and immunosuppression by ESTIMATE and TIDE algorithm. The TGS-high risk group exhibited lower richness and diversity in the T-cell receptor (TCR) repertoire.

This study introduces a novel TGS score developed through LASSO Cox regression analysis, utilizing DEGs in pro- and anti-inflammatory macrophage cells. High TGS tumors exhibited enrichment in TGF-β signaling and hypoxia pathways, suggesting their potential utility in predicting prognosis and immune responses in patients with LUAD. These results offer promising implications for the development of therapeutic strategies for LUAD.

Lung adenocarcinoma (LUAD) is a prevalent malignant tumor of the respiratory tract. The Kelch like ECH associated protein 1 (KEAP1)/nuclear factor erythroid 2-related factor 2 (NRF2)/heme oxygenase 1 (HO-1) axis serves a pivotal role in the occurrence and progression of LUAD. The present study aimed to identify specific genes regulated by mutations of the KEAP1/NRF2/HO-1 axis and to investigate their prognostic potential in LUAD, as well as their association with the tumor microenvironment. Immunohistochemistry was performed to assess the expression levels of KEAP1, NRF2 and HO-1 in LUAD tissues and to evaluate their association with clinical pathology. Sequencing data and clinical information were obtained from The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus (GSE68465) databases, whilst mutation information was sourced from the cBio Cancer Genomics Portal website. The R package 'limma' and Venn diagram were utilized to identify upregulated differentially expressed genes. Subsequently, a prognostic model was constructed using univariate Cox regression analysis and 101 machine learning methods. A nomogram of the prognostic model was generated to assess its efficacy in evaluating survival among patients with LUAD. The 'ImmuCellAI' and 'oncoPredict' R packages were used to compare and evaluate differences in immune cell infiltration and immunotherapy between high- and low-risk groups, as well as the sensitivity of LUAD to chemotherapy drugs. Compared with the group with negative expression, the results revealed that the group with positive expression of NRF2 and HO-1 exhibited advanced tumor, lymph node and clinical stages and a worse prognosis. A predictive model incorporating four genes (kynureninase, serpin family B member 5, insulin like 4 and γ-aminobutyric acid type A receptor subunit α3) was constructed based on KEAP1/NRF2/HO-1 mutation-mediated upregulated genes (KNHMUGs). Risk score was an independent prognostic factor for patients with LUAD (hazard ratio, 1.038; 95% confidence interval, 1.034-1.043; P<0.001). A nomogram was developed to predict the prognosis of patients with LUAD, which was validated as a reliable prognostic tool. The low-risk group exhibited higher immune cell infiltration, including CD4+ T, CD8+ T, natural killer (NK) and NKT cells, compared with the high-risk group. In addition, it demonstrated increased expression levels of immune checkpoint inhibitory genes such as cytotoxic T-lymphocyte associated protein 4, T cell immunoreceptor with Ig and ITIM domains, hepatitis A virus cellular receptor 2 and B and T lymphocyte associated protein. Moreover, it displayed enhanced sensitivity to immunotherapy. Drug sensitivity analysis revealed that the high-risk group exhibited increased sensitivity towards vinblastine, docetaxel and cisplatin, whereas the low-risk group showed increased sensitivity to BMS_754807, SB505124_1194 and JQ1_2172. In conclusion, a KNHMUGs-based gene signature was constructed in the present study, which holds promise as a biomarker for evaluating patient prognosis and guiding treatment by effectively assessing immunotherapy response and chemotherapy sensitivity in patients with LUAD.

The mortality and therapeutic failure in lung adenocarcinoma (LUAD) are mainly resulted from the wide metastasis and chemotherapy resistance. Up to now, accurate and stable predictive prognostic indicator for revealing the progress and novel therapeutic strategies of LUAD is infrequent, nonetheless. Diversified programmed cell death (PCD) has been widely confirmed that participated in the occurrence and development of various malignant tumors, respectively. In this research, we integrated fourteen types of PCD, bulk multi-omic data from TCGA-LUAD and other cohorts in gene expression omnibus (GEO) and clinical LUAD patients to develop our analysis. Consequently, pivotal fourteen PCD genes, especially CAMP, CDK5R1, CTSW, DAPK2, GAB2, GAPDH, GATA2, HGF, MAPT, NAPSA, NUPR1, PIK3CG, PLA2G3, and SLC7A11, were utilized to establish the prognostic signature, namely cell death index (CDI). The validation in several external cohorts indicated that CDI can be regarded as a potential risk factor of LUAD patients. Combined with other common clinical information, a nomogram with potential predictive ability was constructed. Besides, according to the CDI signature, the tumor microenvironment (TME) and sensitivity to some potential chemotherapeutic drugs were further and deeply explored. Notably, verification and functional experiments further demonstrated the remarkable correlation between CDI and unfold protein response. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of LUAD patients.

mRNA expression-based stemness index (mRNAsi) has been used for prognostic assessment in various cancers, but its application in lung adenocarcinoma (LUAD) is limited, which is the focus of this study. Low mRNAsi in LUAD predicted a better prognosis. Eight genes (GNG7, EIF5A, ANLN, FKBP4, GAPDH, GNPNAT1, E2F7, CISH) associated with mRNAsi were screened to establish a risk model. The differentially expressed genes between the high and low risk groups were mainly enriched in the metabolism, cell cycle functions pathway. The low risk score group had higher immune cell scores. Patients with lower TIDE scores in the low risk group had better immunotherapy outcomes. In addition, risk score was effective in assessing drug sensitivity of LUAD. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) data showed that eight genes were differentially expressed in LUAD cell lines, and knockdown of EIF5A reduced the invasion and migration ability of LUAD cells. This study designed a risk model based on the eight mRNAsi-related genes for predicting LUAD prognosis. The model accurately predicted the prognosis and survival of LUAD patients, facilitating the assessment of the sensitivity of patients to immunotherapy and chemotherapy.

Gastric cancer (GC) ranks as one of the most prevalent malignant tumors globally. The subtle manifestation of its early-stage symptoms often results in many GC patients being diagnosed at a late or advanced stage, thereby posing significant obstacles to the effectiveness of chemotherapy treatments. Therefore, identifying early biomarkers for GC is crucial. In recent years, an increasing number of studies have highlighted the pivotal role that aging plays in the progression of cancer. Among the various proteins involved, Cytoskeleton-associated protein 2 (CKAP2) emerges as a crucial player in controlling cell proliferation, regulating mitosis and cell division, and exerting a significant influence on the aging process. We employed a bioinformatics approach to assess the causal association between aging-related genes and GC and explore the potential significance of CKAP2 in GC by analyzing data sourced from various repositories, including Genotype Tissue Expression (GTEx), GWAS Catalog, The Database of Cell Senescence Genes (CellAge), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Human Protein Atlas (HPA), and the Comparative Toxicology Genome Database (CTD). Our research summarized the causal relationship between CKAP2 expression and the development risk of GC, differential expression in GC, the relationship with the prognosis of GC, genetic correlation, functional analysis, and immune cell infiltration, and explored the interaction of CKAP2 and chemical substances. The findings revealed that an elevation in CKAP2 expression correlated with a reduced likelihood of developing GC. There was a significant difference in the expression of CKAP2 between GC and normal patients. Specifically, there was higher expression in GC compared to normal patients. In addition, CKAP2 has been proven to have diagnostic value in GC, and elevated levels of CKAP2 expression are indicative of a more favorable prognosis. Immune infiltration analysis revealed the relationship between CKAP2 and tumor immune microenvironment, while the Comparative Toxicology Genome Database (CTD) identified a small molecule compound that may target CKAP2. In summary, through comprehensive multivariate analyses, we identified and validated the potential role that CKAP2 may play in GC. Therefore, CKAP2 shows potential as an indicator for both the diagnosis and prognosis of GC, making it worthy of further clinical investigation.

We aimed to create a NK cell marker genes-based signature to predict immunotherapy response and prognosis in colorectal cancer. We integrated scRNA-seq data from four Gene Expression Omnibus (GEO) samples and performed Weighted gene correlation network analysis (WGCNA) based on 587 the Cancer Genome Atlas (TCGA) colorectal cancer samples to uncover NK cell-related genes. We identified 1080 NK cell-related core genes and 276 NK cell-related feature genes based on WGCNA and clustering and annotation of scRNA-seq data, respectively. Six key NK cell-related prognostic signature genes were obtained by univariate and LASSO regression analyses, including ADAM8, CTSD, CCL4, IL2RB, TTC38, and PLEK. Two validation cohorts from the GEO dataset, comprising 124 and 201 samples respectively, were used. The signature was significantly associated with overall survival and correlated with immune cell infiltration, immune and stromal scores, and immune checkpoint genes. Furthermore, the signature was associated with the homologous recombination deficiency (HRD) and T-cell receptor (TCR) scores. In conclusion, our study proposes a new prognostic signature based on NK cell marker genes, which may serve as a potential tool to predict overall survival and immunotherapy response for CRC patients.

Mitochondrial genes are involved in the tumor metabolism of ovarian cancer (OC), affecting immune cell infiltration and treatment response. We aimed to utilize mitochondrial genes to predict OC prognosis and immunotherapy response.

The prognosis data, immunotherapy efficacy and next generation sequencing data of OC patients were downloaded from The Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO). Mitochondrial genes were sourced from the MitoCarta3.0 database. Seventy percent of the patients were randomly selected as the discovery cohort for model construction, while the remaining 30% constituted the validation cohort for model assessment. Using the expression of mitochondrial genes as the predictor variable and based on the neural network algorithm, the overall survival (OS) time and immunotherapy efficacy (complete or partial response) of the included patients were predicted.

There were 375 OC patients included to construct the prognostic model, and 26 patients were included to construct the immune efficacy model. The average area under the receiver operating characteristic curve (AUC) of the prognostic model was: 0.7268 [95% confidence interval (CI), 0.7258-0.7278] in the discovery cohort and 0.6475 (95% CI: 0.6466-0.6484) in the validation cohort. The average AUC of the immunotherapy efficacy model was: 0.9444 (95% CI: 0.8333-1.0000) in the discovery cohort and 0.9167 (95% CI: 0.6667-1.0000) in the validation cohort.

The application of mitochondrial genes and neural networks shows potential in predicting the prognosis and immunotherapy response in OC patients. And this approach could provide valuable insights for personalized treatment strategies.

Mitochondrial genes are involved in tumor metabolism in ovarian cancer (OC) and affect immune cell infiltration and treatment responses.

To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.

Prognosis, immunotherapy efficacy, and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Mitochondrial genes were sourced from the MitoCarta3.0 database. The discovery cohort for model construction was created from 70% of the patients, whereas the remaining 30% constituted the validation cohort. Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm, the overall survival time and immunotherapy efficacy (complete or partial response) of patients were predicted.

In total, 375 patients with OC were included to construct the prognostic model, and 26 patients were included to construct the immune efficacy model. The average area under the receiver operating characteristic curve of the prognostic model was 0.7268 [95% confidence interval (CI): 0.7258-0.7278] in the discovery cohort and 0.6475 (95%CI: 0.6466-0.6484) in the validation cohort. The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444 (95%CI: 0.8333-1.0000) in the discovery cohort and 0.9167 (95%CI: 0.6667-1.0000) in the validation cohort.

The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC, providing valuable insights into personalized treatment strategies.

Thyroid carcinoma (THCA) is the most common cancer of the endocrine system. Natural killer (NK) cell play an important role in tumor immune surveillance. The aim of this study was to explore the possible molecular mechanisms involved in NK cell in THCA to help the management and treatment of the disease.

All data were downloaded from public databases. Candidate hub genes associated with NK cell in THCA were identified by limma, WGCNA and singleR packages. Functional enrichment analysis was performed on the candidate hub genes. Hub genes associated with NK cell were identified by Pearson correlation analysis. The mRNA-miRNA-lncRNA and transcription factors (TF) networks were constructed and the drug was predicted.

The infiltration level of NK cell in THCA tissues was higher than that in paracancerous tissues. KEGG functional enrichment analysis only obtained two signaling pathways, thyroid hormone synthesis and mineral absorption. CTSC, FN1, SLC34A2 and TMSB4X identified by Pearson correlation analysis were considered as the hub genes. Receiver operating characteristic analysis suggested that hub genes may be potential diagnostic biomarkers. In mRNA-miRNA-lncRNA network, FN1 had the highest correlation with IQCH-AS1, and IQCH-AS1 was also correlated with hsa-miR-543. In addition, FN1 and RUNX1 were also found to have the highest correlation in TF network. Finally, NK cell-related drugs belinostat and vorinostat were identified based on ASGARD.

The identification of important signaling pathways, molecules and drugs provides potential research directions for further research in THCA and contributes to the development of diagnostic and therapeutic approaches for this disease.

Lung cancer is a frequent malignancy with a poor prognosis. Extensive metabolic alterations are involved in carcinogenesis and could, therefore, serve as a reliable prognostic phenotype.

Our study aimed to develop a prognosis signature and explore the relationship between metabolic characteristic-related signature and immune infiltration in lung adenocarcinoma (LUAD).

TCGA-LUAD and GSE31210 datasets were used as a training set and a validation set, respectively.

A total of 513 LUAD samples collected from The Cancer Genome Atlas database (TCGA-LUAD) were used as a training dataset. Molecular subtypes were classified by consensus clustering, and prognostic genes related to metabolism were analyzed based on Differentially Expressed Genes (DEGs), Protein-Protein Interaction (PPI) network, the univariate/multivariate- and Lasso- Cox regression analysis.

Two molecular subtypes with significant survival differences were divided by the metabolism gene sets. The DEGs between the two subtypes were identified by integrated analysis and then used to develop an 8-gene signature (TTK, TOP2A, KIF15, DLGAP5, PLK1, PTTG1, ECT2, and ANLN) for predicting LUAD prognosis. Overexpression of the 8 genes was significantly correlated with worse prognostic outcomes. RiskScore was an independent factor that could divide LUAD patients into low- and high-risk groups. Specifically, high-risk patients had poorer prognoses and higher immune escape. The Receiver Operating Characteristic (ROC) curve showed strong performance of the RiskScore model in estimating 1-, 3- and 5-year survival in both training and validation sets. Finally, an optimized nomogram model was developed and contributed the most to the prognostic prediction in LUAD.

The current model could help effectively identify high-risk patients and suggest the most effective drug and treatment candidates for patients with LUAD.

Triple-negative breast cancer (TNBC) is an aggressive disease with a poor prognosis and lack of effective treatment. In this study, TNBCs were analyzed from the perspective of tumor stemness based on scRNA-seq data. The analysis showed that tumor cells of TNBC were divided into 4 subtypes, with subtype 2 having the highest stemness score. A prognostic model of 7 tumor stemness-related genes (AP2S1, CHML, FABP7, FADS2, PAXX, SDC1 and TOP2A) was developed based on marker genes of this subtype and TCGA data, and the predictive power of this feature was well validated in different clinical subgroups. TNBC patients in the low TS group had a better prognosis. In addition, drug sensitivity analysis showed that patients in the high TS (tumor stemness) score group were more sensitive to PD-L1 inhibitors and the chemotherapeutic agents. In conclusion, our study developed a prognostic model based on TNBC tumor stemness cell marker genes, which has a good ability to predict the prognosis of TNBC patients and the effect of response to drug therapy.

Lung adenocarcinoma (LUAD) is a major contributor to cancer-related deaths, distinguished by its pronounced tumor heterogeneity and persistent challenges in overcoming drug resistance. In this study, we utilized single-cell RNA sequencing (scRNA-seq) to dissect the roles of programmed cell death (PCD) pathways, including apoptosis, necroptosis, pyroptosis, and ferroptosis, in shaping LUAD heterogeneity, immune infiltration, and prognosis. Among these, ferroptosis and pyroptosis were most significantly associated with favorable survival outcomes, highlighting their potential roles in enhancing anti-tumor immunity. Distinct PCD-related LUAD subtypes were identified, characterized by differential pathway activation and immune cell composition. Subtypes enriched with cytotoxic lymphocytes and dendritic cells demonstrated improved survival outcomes and increased potential responsiveness to immunotherapy. Drug sensitivity analysis revealed that these subtypes exhibited heightened sensitivity to targeted therapies and immune checkpoint inhibitors, suggesting opportunities for personalized treatment strategies. Our findings emphasize the interplay between PCD pathways and the tumor microenvironment, providing insights into the mechanisms underlying tumor drug resistance and immune evasion. By linking molecular and immune features to clinical outcomes, this study highlights the potential of targeting PCD pathways to enhance therapeutic efficacy and overcome resistance in LUAD. These results contribute to a growing framework for developing precise and adaptable cancer therapies tailored to specific tumor characteristics.

There remains ongoing debate regarding the association of homologous recombination deficiency (HRD) with patient survival across various malignancies, highlighting the need for a comprehensive understanding of HRD's role in different cancer types. Based on data from databases, we conducted a multivariable omics analysis on HRD in 33 cancer types, focusing mainly on 23 cancers in which HRD was significantly associated with patient overall survival (OS) rates. This analysis included the mechanisms related to patient prognosis, gene expression, gene mutation, and signaling pathways. In this study, HRD was found to be significantly associated with patient prognosis, but its impact varied among different cancers. HRD was linked to different outcomes for patients with distinct tumor subtypes and was correlated with clinical features such as clinical stage and tumor grade. Driver gene mutations, including TP53, MUC4, KRAS, HRAS, FLG, ANK3, BRCA2, ATRX, FGFR3, NFE2L2, MAP3K1, PIK3CA, CIC, FUBP1, ALB, CTNNB1, and MED12, were associated with HRD across specific cancer types. We also analyzed differentially expressed genes (DEGs) and differentially methylated regions (DMRs) in relation to HRD levels in these cancers. Furthermore, we explored the correlation between HRD and signaling pathways, as well as immune cell infiltration. Overall, our findings contribute to a comprehensive understanding of HRD's multifaceted role in cancer.

Efferocytosis (ER) refers to the process of phagocytic clearance of programmed dead cells, and studies have shown that it is closely related to tumor immune escape.

This study was based on a comprehensive analysis of TCGA, GEO and CTRP databases. ER-related genes were collected from previous literature, univariate Cox regression was performed and consistent clustering was performed to categorize lung adenocarcinoma (LUAD) patients into two subgroups. Lasso regression and multivariate Cox regression analyses were used to construct ER-related prognostic features, and multiple immune infiltration algorithms were used to assess the correlation between the extracellular burial-related risk score (ERGRS) and tumor microenvironment (TME). And the key gene HAVCR1 was identified by deep learning, etc. Finally, pan-cancer analysis of the key genes was performed and in vitro experiments were conducted to verify the promotional effect of HAVCR1 on LUAD progression.

A total of 33 ER-related genes associated with the prognosis of LUAD were identified, and the prognostic signature of ERGRS was successfully constructed to predict the overall survival (OS) and treatment response of LUAD patients. The high-risk group was highly enriched in some oncogenic pathways, while the low-ERGRS group was highly enriched in some immune-related pathways. In addition, the high ERGRS group had higher TMB, TNB and TIDE scores and lower immune scores. The low-risk group had better immunotherapeutic response and less likelihood of immune escape. Drug sensitivity analysis revealed that BRD-K92856060, monensin and hexaminolevulinate may be potential therapeutic agents for the high-risk group. And ERGRS was validated in several cohorts. In addition, HAVCR1 is one of the key genes, and knockdown of HAVCR1 in vitro significantly reduced the proliferation, migration and invasion ability of lung adenocarcinoma cells.

Our study developed a novel prognostic signature of efferocytosis-related genes. This prognostic signature accurately predicted survival prognosis as well as treatment outcome in LUAD patients and explored the role of HAVCR1 in lung adenocarcinoma progression.

Innate lymphoid cells (ILCs) exert tumor suppressive and tumor promoting effects. However, the prognostic significance of ILC-associated genes remains unclear in hepatocellular carcinoma (HCC). Hence, the aim of this research was to develop an innovative predictive risk classification system using bioinformatics examination.

We explored the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to gather data pertaining to HCC and its clinical details. Significantly different ILC-associated genes were investigated by Seurat analysis. The number of signaling interactions of ILCs with other cells was discovered by CellPhoneDB analysis. ClusterProfiler and Metascape were utilized to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on ILC genes. In order to identify potential ILC predictors, we utilized univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses, subsequently validating these predictors in TCGA and GEO groups. The multi-omics ILC signature model's clinical predictive capabilities, along with drug sensitivity and immune factor relations, were assessed using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and pRRophetic. We investigated the possible molecular pathways in our predictive ILC signature through the utilization of gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Five key genes were screened out by constructing a competing endogenous RNA (ceRNA) network using Cytoscape and their values in clinical indexes were demonstrated. Immunohistochemistry (IHC) in HCC cases confirmed the expression of these genes.

ILC cell subsets were identified, and cell-cell communication analysis revealed that the signaling pathways involving ILC cell subsets were mostly abundant in the HCC microenvironment. Subsequently, 270 marker genes of the ILC clusters were subjected to GO and KEGG enrichment analysis. Furthermore, a total of 58 prognostically relevant genes were screened as features for prognostic prediction models. Next, the models were validated and clinically evaluated (P values of Kaplan-Meier survival curves below 0.05). Five key genes (C2, STK4, CALM1, IL7R, and RORA) were further screened by multi omics analysis of immune cell and factor and drug sensitivity and correlation analysis of tumor regulatory genes in liver cancer. Furthermore, the potential clinical value of the 5 key genes was confirmed in HCC patients. Finally, the IHC results confirmed the expression of C2, STK4, CALM1, IL7R, and RORA in HCC. Our experimental results provided preliminary evidence supporting the oncogenic roles of STK4 and CALM1, as well as the tumor-suppressive roles of C2, RORA, and IL7R in HCC.

A novel prognostic feature of ILC potentially involved in HCC was discovered. It showed high values in predicting patient overall survival (OS) as well as good differences in immunity and drug sensitivity. Therefore, targeting these ILC signatures may be a potential effective approach in HCC treatment.

Natural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes.

The data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC).

Tumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC.

We developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited.

We conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared.

The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group.

This study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

The role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy.

This study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS.

MCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and β-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors.

The combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized.

We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database.

We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups.

Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

Bladder cancer (BLCA) is a highly aggressive and heterogeneous disease, posing challenges for diagnosis and treatment. Cancer immunotherapy has recently emerged as a promising option for patients with advanced and drug-resistant cancers. Fibroblasts, a significant component of the tumor microenvironment, play a crucial role in tumor progression, but their precise function in BLCA remains uncertain.

Single-cell RNA sequencing (scRNA-seq) data for BLCA were obtained from the Gene Expression Omnibus database. The R package "Seurat" was used for processing scRNA-seq data, with uniform manifold approximation and projection (UMAP) for downscaling and cluster identification. The FindAllMarkers function identified marker genes for each cluster. Differentially expressed genes influencing overall survival (OS) of BLCA patients were identified using the limma package. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between high- and low-risk groups were investigated. RT-qPCR and immunohistochemistry validated the expression of prognostic genes.

Fibroblast marker genes identified three molecular subtypes in the testing set. A prognostic signature comprising ten genes stratified BLCA patients into high- and low-score groups. This signature was validated in one internal and two external validation sets. High-score patients exhibited increased immune cell infiltration, elevated chemokine expression, and enhanced immune checkpoint expression but had poorer OS and a reduced response to immunotherapy. Six sensitive anti-tumor drugs were identified for the high-score group. RT-qPCR and immunohistochemistry showed that CERCAM, TM4SF1, FN1, ANXA1, and LOX were highly expressed, while EMP1, HEYL, FBN1, and SLC2A3 were downregulated in BLCA.

A novel fibroblast marker gene-based signature was established, providing robust predictions of survival and immunotherapeutic response in BLCA patients.

Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.

Improving immunotherapy efficacy for EGFR-negative lung adenocarcinoma (LUAD) patients remains a critical challenge, and the therapeutic effect of immunotherapy is largely determined by the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are the top-ranked immune infiltrating cells in the TME, and M2-TAMs exert potent roles in tumor promotion and chemotherapy resistance. An M2-TAM-based prognostic signature was constructed by integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to reveal the immune landscape and select drugs in EGFR-negative LUAD.

M2-TAM-based biomarkers were obtained from the intersection of bulk RNA-seq data and scRNA-seq data. After consensus clustering of EGFR-negative LUAD into different clusters based on M2-TAM-based genes, we compared the prognosis, clinical features, estimate scores, immune infiltration, and checkpoint genes among the clusters. Next, we combined univariate Cox and LASSO regression analyses to establish an M2-TAM-based prognostic signature.

CCL20, HLA-DMA, HLA-DRB5, KLF4, and TMSB4X were verified as prognostic M2-like TAM-related genes by univariate Cox and LASSO regression analyses. IPS and TMB analyses revealed that the high-risk group responded better to common immunotherapy.

The study shows the potential of the M2-like TAM-related gene signature in EGFR-negative LUAD, explores the immune landscape based on M2-like TAM-related genes, and predict immunotherapy response of patients with EGFR-negative LUAD, providing a new insight for individualized treatment.

Immunosuppression by the tumor microenvironment is a pivotal factor contributing to tumor progression and immunotherapy resistance. Priming the tumor immune microenvironment (TIME) has emerged as a promising strategy for improving the efficacy of cancer immunotherapy. In this study we investigated the effects of noninvasive radiofrequency radiation (RFR) exposure on tumor progression and TIME phenotype, as well as the antitumor potential of PD-1 blockage in a model of pulmonary metastatic melanoma (PMM). Mouse model of PMM was established by tail vein injection of B16F10 cells. From day 3 after injection, the mice were exposed to RFR at an average specific absorption rate of 9.7 W/kg for 1 h per day for 14 days. After RFR exposure, lung tissues were harvested and RNAs were extracted for transcriptome sequencing; PMM-infiltrating immune cells were isolated for single-cell RNA-seq analysis. We showed that RFR exposure significantly impeded PMM progression accompanied by remodeled TIME of PMM via altering the proportion and transcription profile of tumor-infiltrating immune cells. RFR exposure increased the activation and cytotoxicity signatures of tumor-infiltrating CD8+ T cells, particularly in the early activation subset with upregulated genes associated with T cell cytotoxicity. The PD-1 checkpoint pathway was upregulated by RFR exposure in CD8+ T cells. RFR exposure also augmented NK cell subsets with increased cytotoxic characteristics in PMM. RFR exposure enhanced the effector function of tumor-infiltrating CD8+ T cells and NK cells, evidenced by increased expression of cytotoxic molecules. RFR-induced inhibition of PMM growth was mediated by RFR-activated CD8+ T cells and NK cells. We conclude that noninvasive RFR exposure induces antitumor remodeling of the TIME, leading to inhibition of tumor progression, which provides a promising novel strategy for TIME priming and potential combination with cancer immunotherapy.

Lung cancer is one of the most prevalent malignancies worldwide, contributing to over a million cancer-related deaths annually. Despite extensive research investigating the genetic factors associated with lung cancer susceptibility and prognosis, few studies have explored genetic predispositions regarding the immune system. This review discusses the most recent genomic findings related to the susceptibility to or protection against lung cancer, patient survival, and therapeutic responses. The results demonstrated the effect of immunogenetic variations in immune system-related genes associated with innate and adaptive immune responses, cytokine, and chemokine secretions, and signaling pathways. These genetic diversities may affect the crosstalk between tumor and immune cells within the tumor microenvironment, influencing cancer progression, invasion, and prognosis. Given the considerable variability in the individual immunegenomics profiles, future studies should prioritize large-scale analyses to identify potential genetic variations associated with lung cancer using highthroughput technologies across different populations. This approach will provide further information for predicting response to targeted therapy and promotes the development of new measures for individualized cancer treatment.

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. In recent years, immunotherapy has greatly changed the treatment pattern of advanced LUAD. However, only a small proportion of LUAD patients benefitted from immune checkpoint inhibitor therapy. There is an urgent need to develop a biomarker to predict immune therapy response. E2F7 has been shown to be closely related to immune cell infiltration and immune checkpoint expression in tumors. However, it is unclear whether the E2F7 expression is related to the immunotherapy efficacy in LUAD. Therefore, we conducted this study to investigate the clinical characteristics, function, and immunotherapy responsiveness of E2F7 expression, and to explore the potential of E2F7 as an immunotherapy response biomarker in LUAD. We analyzed the clinical characteristics and biological function of E2F7 expression based on data from the Cancer Genome Atlas and Gene Expression Omnibus database. In addition, we used single-cell sequencing data to analyze the immune regulatory effects of E2F7 in LUAD. Furthermore, we analyzed the immunotherapy response prediction ability of E2F7 expression based on the immunotherapy database. Compared to normal lung tissue, E2F7 was specifically overexpressed in LUAD, and its expression was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. E2F7 was enriched in cell division and cell cycle functions. In addition, the expressions of immune checkpoints were correlated with the E2F7 expression. E2F7 was highly expressed in myeloid cells, and E2F7 highly expressed myeloid cells were associated with immune and inflammatory responses. Moreover, the expression level of E2F7 can effectively distinguish different immune therapy responses in LUAD patients. E2F7 was upregulated in LUAD, and high expression of E2F7 was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. Moreover, E2F7 may exert its immunosuppressive effect by affecting the function of myeloid cells. These results indicated the potential role of E2F7 as a biomarker for predicting LUAD immunotherapy responses.

The role of RNA-binding fox one homolog 2 (RBFOX2) in the progression of multiple tumors is increasingly supported by evidence. However, the unclearness pertaining to the expression of RBFOX2, its prognostic potential, and its correlation with the tumor microenvironment (TME) in pan-cancer persists. This study aims to comprehensively investigate the immunological prognostic value of RBFOX2.

The Cancer Genome Atlas Gene Expression Omnibus Genotype-Tissue Expression (GTEx), TIMER2.0, Kaplan-Meier (K-M) Plotter, University of Alabama at Birmingham Cancer data analysis Portal (UALCAN), cbioportal, and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) were utilized for a systematic analysis of RBFOX2. This analysis included studying its expression, prognostic value, DNA methylation, enrichment analysis, immune infiltration cells, and immune-related genes. Additionally, qRT-PCR, CCK-8, colony formation, transwell assays, and immunohistochemistry were employed to analyze the expression and biological function of RBFOX2 in liver cancer.

Variations in RBFOX2 expression have been observed across diverse tumors and have been identified as indicators of unfavorable prognosis. It is closely linked to immune infiltration cells, immune checkpoints, chemokines, and chemokine receptors in the TME. Higher levels of RBFOX2 have been significantly associated with low response and poor prognosis in patients with non-small cell lung cancer (NSCLC) and melanoma who receive immunotherapy. Furthermore, the DNA methylation of RBFOX2 varies across different types of cancer and has shown better prognosis in patients with BLCA, BRCA, CESC, COAD, DLBC, HNSC, LAML, LGG, LUAD, PAAD, SKCM and THYM. Interestingly, RBFOX2 expression was found to be lower in hepatocellular carcinoma (HCC) patients' tumor tissues compared to their paired adjacent tissues. In vitro studies have shown that knockdown of RBFOX2 significantly promotes the growth and metastasis of liver cancer cells.

This study investigates the correlation between DNA methylation, prognostic value, and immune cell infiltration with the expression of RBFOX2 in pan-cancer and indicates its potential role to inhibit metastasis of liver cancer.

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

This study aimed to type breast cancer in relation to reactive oxygen species (ROS), clinical indicators, single nucleotide variant (SNV) mutations, functional differences, immune infiltration, and predictive responses to immunotherapy or chemotherapy, and constructing a prognostic model.

We used uniCox analysis, ConsensusClusterPlus, and the proportion of ambiguous clustering (PAC) to analyze The Cancer Genome Atlas (TCGA) data to determine optimal groupings and obtain differentially expressed ROS-related genes. Clinical indicators were then combined with the classification results and the Chi-square test was used to assess differences. We further examined SNV mutations, and functional differences using gene set enrichment analysis (GSEA) analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, immune cell infiltration, and response to immunotherapy and chemotherapy. A prognostic model for breast cancer was constructed using these differentially expressed genes, immunotherapy or chemotherapy responses, and survival curves. RT-qPCR was used to detect the differences in the expression of LCE3D, CA1, PIRT and SMR3A in breast cancer cell lines and normal breast epithelial cell line.

We identified two distinct tumor types with significant differences in ROS-related gene expression, clinical indicators, SNV mutations, functional pathways, and immune infiltration. The response to specific chemotherapy drugs and immunotherapy treatments also documented significant differences. The prognostic model constructed with 16 genes linked to survival could efficiently divide patients into high- and low-risk groups. The high-risk group showed a poorer prognosis, higher tumor purity, distinct immune microenvironment, and lower immunotherapy response. RT-qPCR results showed that LCE3D, CA1, PIRT and SMR3A are highly expressed in breast cancer.

Our methodical examination presented an enhanced insight into the molecular and immunological heterogeneity of breast cancer. It can contribute to the understanding of prognosis and offer valuable insights for personalized treatment strategies. Further, the prognostic model can potentially serve as a powerful tool for risk stratification and therapeutic decision-making in clinical settings.

Cancer-associated fibroblasts (CAFs) play a significant role in regulating the clinical outcome and radiotherapy prognosis of prostate cancer (PCa). The aim of this study is to identify CAFs-related genes (CAFsRGs) using single-cell analysis and evaluate their potential for predicting the prognosis and radiotherapy prognosis in PCa.

We acquire transcriptome and single-cell RNA sequencing (scRNA-seq) results of PCa and normal adjacent tissues from The GEO and TCGA databases. The "MCPcounter" and "EPIC" R packages were used to assess the infiltration level of CAFs and examine their correlation with PCa prognosis. ScRNA-seq and differential gene expression analyses were used to extract CAFsRGs. We also applied COX and LASSO analysis to further construct a risk score (CAFsRS) to assess biochemical recurrence-free survival (BRFS) and radiotherapy prognosis of PCa. The predictive efficacy of CAFsRS was evaluated by ROC curves and subgroup analysis. Finally, we integrated the CAFsRS gene signature with relevant clinical features to develop a nomogram, enhancing the predictive accuracy.

The abundance of CAFs is associated with a poor prognosis of PCa patients. ScRNA-seq and differential gene expression analysis revealed 323 CAFsRGs. After COX and LASSO analysis, we obtained seven CAFsRGs with prognostic significance (PTGS2, FKBP10, ENG, CDH11, COL5A1, COL5A2, and SRD5A2). Additionally, we established a risk score model based on the training set (n = 257). The ROC curve was used to confirm the performance of CAFsRS (The AUC values for 1, 3 and 5-year survival were determined to be 0.732, 0.773, and 0.775, respectively.). The testing set (n = 129), GSE70770 set (n = 199) and GSE116918 set (n = 248) revealed that the model exhibited exceptional predictive performance. This was also confirmed by clinical subgroup analysis. The violin plot demonstrated a statistically significant disparity in the CAFs infiltrations between the high-risk and low-risk groups of CAFsRS. Further analysis confirmed that both CAFsRS and T stage were independent prognostic factors for PCa. The nomogram was then established and its excellent predictive performance was demonstrated through calibration and ROC curves. Finally, we developed an online prognostic prediction app ( https://sysu-symh-cafsnomogram.streamlit.app/ ) to facilitate the practical application of the nomogram.

The prognostic prediction risk score model we constructed could accurately predict BRFS and radiotherapy prognosis PCa, which can provide new ideas for clinicians to develop personalized PCa treatment and follow-up programs.

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

Natural killer (NK) cells play essential roles in the tumor development, diagnosis, and prognosis of tumors. In this study, we aimed to establish a reliable signature based on marker genes in NK cells, thus providing a new perspective for assessing immunotherapy and the prognosis of patients with gastric cancer (GC). We analyzed a total of 1560 samples retrieved from the public database. We performed a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data of gastric cancer and identified 377 marker genes for NK cells. By performing Cox regression analysis, we established a 12-gene NK cell-associated signature (NKCAS) for the Cancer Genome Atlas (TCGA) cohort, that assigned GC patients into a low-risk group (LRG) or a high-risk group (HRG). In the TCGA cohort, the areas under curve (AUC) value were 0.73, 0.81, and 0.80 at 1, 3, and 5 years. External validation of the predictive ability for the signature was then validated in the Gene Expression Omnibus (GEO) cohorts (GSE84437). The expression levels of signature genes were measured and validated in GC cell lines by real-time PCR. Moreover, NKCAS was identified as an independent prognostic factor by multivariate analysis. We combined this with a variety of clinicopathological characteristics (age, M stage, and tumor grade) to construct a nomogram to predict the survival outcomes of patients. Moreover, the LRG showed higher immune cell infiltration, especially CD8+ T cells and NK cells. The risk score was negatively associated with inflammatory activities. Importantly, analysis of the independent immunotherapy cohort showed that the LRG had a better prognosis and immunotherapy response when compared with the HRG. The identification of NK cell marker genes in this study suggests potential therapeutic targets. Additionally, the developed predictive signatures and nomograms may aid in the clinical management of GC.