The emergence of immuno-oncology (IO) has led to revolutionary changes in the field of cancer treatment. Despite notable advancements in this field, a thorough exploration of its full depth and extent has yet to be performed. This study provides a comprehensive overview of publications pertaining to IO. Publications on IO from 2014 to 2023 were retrieved by searching the Web of Science Core Collection database (WoSCC). VOSviewer software and Citespace software were used for the visualized analysis. A total of 1,874 articles have been published in the IO domain. The number of publications and citations has been increasing annually. This study also examines the primary research directions within the field of IO. In conclusion, this study offers a comprehensive overview of the opportunities and challenges associated with IO, illuminating the current status of research and indicating potential future trajectories in this rapidly progressing field. This study provides a comprehensive survey of the current research status and hot spots within the field of IO. It will assist researchers in comprehending the current research emphasis and development trends in this field and offers guidance for future research directions.

Renal cell carcinoma (RCC) is a heterogeneous malignancy with diverse gene expression patterns, molecular landscapes, and differentiation characteristics of tumor cells. It is imperative to develop molecular RCC classification based on tumor cell differentiation for precise risk stratification and personalized therapy.

We obtained scRNA-seq profiles from GSE159115 and bulk RNA-seq profiles from TCGA-KIRC cohort. We then performed scRNA-seq cluster analysis, monocle2 pseudotime analysis, and prognostic analysis to obtain tumor cell differentiation-related prognostic genes (TCDGs). Subsequently, we conducted consensus clustering to construct the RCC tumor cell differentiation-related prognostic classification (RCC-TCDC) and implemented prognostic and multi-omics analyses. Moreover, we utilized Lasso regression to help develop a multivariable prognostic model. In addition, we performed correlation analysis and Cmap algorithm for regulatory network establishment and candidate inhibitor prediction. We eventually included 370 kidney neoplasm patients in Xinhua cohort to undergo immunohistochemical staining and scoring for classification and comprehensive statistical analyses, including Chi-square tests, Kaplan-Meier survival analyses, and multivariable Cox regression analysis .

32 TCDGs were identifiedand RCC-TCDC was constructed to classify TCGA-KIRC patients into RCC-low differentiation (RCC-LD) (S100A11+ SH3BGRL3+, high risk), RCC-moderate differentiation (TSPAN7+, medium risk), and RCC-high differentiation (RCC-HD) (AQP1+ NPR3+, low risk). Notably, RCC-LD was validated as anindependent risk factor for both OS (p = 0.015, HR = 14.0, 95%CI = 1.67-117.8) and PFS (p = 0.010, HR = 4.0, 95%CI = 1.39-11.7) of RCC patients in Xinhua cohort, taking RCC-HD as reference.

We constructed and validated a robust molecular classification system, RCC-TCDC, elucidating three distinct RCC subtypes.

Non-small cell lung cancer (NSCLC) is a fatal disease, and radioresistance is an important factor leading to treatment failure and disease progression. The objective of this research was to detect radioresistance-related genes (RRRGs) with prognostic value in NSCLC.

The weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were performed to identify RRRGs using expression profiles from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. Then, Immune landscape and drug sensitivity were evaluated. The biological functions exerted by the key gene LBH were verified by in vitro experiments.

Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2 and TSPAN2) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. The immune landscape and sensitivity to anti-tumour drugs showed significant disparities between patients categorized into different risk score subgroups. In vitro experiments indicated that overexpression of LBH enhanced the radiosensitivity of A549 cells, and knockdown LBH reversed the cytotoxicity induced by X-rays.

Our study developed an eight-gene risk score model with potential clinical value that can be adopted for choice of drug treatment and prognostic prediction. Its clinical routine use may assist clinicians in selecting more rational practices for individuals, which is important for improving the prognosis of NSCLC patients. These findings also provide references for the development of potential therapeutic targets.

Endoplasmic reticulum stress (ERS) has been implicated in the pathogenesis of various cancers, including colon cancer, by regulating tumor cell survival, growth, and immune response. However, the specific genes involved in ERS that could serve as prognostic markers in colon cancer remain underexplored. This study aims to identify and validate endoplasmic reticulum stress related genes (ERSRGs) in colon cancer that correlate with patient prognosis, thereby enhancing the understanding of ERS in oncological outcomes and potential therapeutic targeting. We utilized bioinformatics analyses to identify ERSRGs from publicly available colon cancer datasets. Differential expression analysis and survival analysis were performed to assess the prognostic significance of these genes. Validation was conducted through quantitative real-time PCR (RT-qPCR) on selected colon cancer cell lines. Our study identified nine ERS related genes (ASNS, ATF4, ATF6B, BOK, CLU, DDIT3, MANF, SLC39A14, TRAF2) involved in critical pathways including IL-12, PI3K-AKT, IL-7, and IL-23 signaling, and linked to 1-, 3-, and 5-year survival of patients with colon cancer. A multivariate Cox model based on these ERS related genes demonstrated significant prognostic power. Further, TRAF2 strong correlated with immune cells infiltration, suggesting its potential roles in modulating immune responses in the tumor microenvironment. The RT-qPCR validation confirmed the differential expression of these genes in human colon cancer cell lines versus human normal colonic epithelial cell line. The identified ERSRGs could serve as valuable prognostic markers and may offer new insights into the therapeutic targeting of ERS in colon cancer.

In this study, we utilized the public database along with single-cell genomics techniques to systematically analyze the expression patterns and clinical significance of key genes in the nicotinamide metabolism pathway in liver cancer samples. The findings indicate that differential nicotinamide metabolism-related key genes are expressed in liver cancer samples. The liver cancer samples were put into separate subgroups using consistency clustering analysis based on differential gene expression levels observed. Additionally, immune infiltration and drug sensitivity analysis also revealed differences between the two subgroups. Survival analysis suggested that the key genes were associated with prognosis. Finally, a prognostic model was established using the key genes, offering a fresh viewpoint on the molecular mechanism investigating liver cancer. This study demonstrated the significant correlation between key genes in the nicotinamide metabolism pathway and the occurrence and progression of liver cancer and indicated that these key genes could serve as prognostic markers and tailored treatment targets for liver cancer.

BAF complexes (BAFs), ATP-dependent regulators of chromatin structure, play a significant role in cancer progression. This pan-cancer study aimed to decode the potential of specific BAFs in the pathology, immunity, and therapy of targeted cancers.

Data were retrieved from The Cancer Genome Atlas, Gene Expression Omnibus, and IMvigor210 databases and were analyzed for expression patterns, prognostic value, mutational signatures, biological pathways, tumor immune microenvironment (TIME) remodeling, and therapeutic resistance of BAFs. Experimental validation was also conducted.

BAFs exhibit abnormal expression in various human cancers. The BAFs model and nomogram (based on multiple variables) were developed as prognostic tools. BAFs regulate the TIME and influence the response to anti-PD-L1 therapy, particularly through ACTL6A, as observed in RNA sequencing and single-cell RNA sequencing datasets (high-resolution gene expression data at the single-cell level). ACTLA6 is a major adverse gene in the prognostic model. Patients with high ACTL6A expression showed significantly worse overall survival (hazard ratio = 1.32, 95 % CI: 1.26-1.39, p < 0.001). ACTL6A expression escalates with breast cancer (BRCA) malignancy, particularly in triple-negative BRCA (TNBC), and correlates with immune checkpoint expression while playing a crucial role in promoting cancer metastasis in TNBC.

Our findings first emphasize the significance of a novel BAFs model for patient prognosis and corroborate the considerable role of BAFs as immune-related biomarkers in pan-cancer progression. ACTL6A has a dual role as an immune-related biomarker and potential therapeutic target in TNBC, deepening our comprehension of its function as an oncogene.

The immune microenvironment significantly influences neural regeneration in spinal cord injury (SCI). Lactate activates central nervous system (CNS) glial cells, prompting the secretion of proinflammatory cytokines and triggering an inflammatory response. Mesenchymal stem cells (MSCs) make a promising future for SCI therapy due to their immune regulation and anti-inflammatory properties. However, it is unclear whether MSCs inhibit inflammatory responses in the SCI microenvironment through lactylation regulation. This study aimed to identify lactylation-related genes (LRGs) in SCI and investigate their role in immune cell infiltration and MSC-mediated inflammation reduction.

Transcription datasets of SCI patients were acquired from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) underwent functional enrichment analysis, and CIBERSORT assessed immune cell infiltration in SCI. Crucial lactylation-related differentially expressed genes (LRDEGs) associated with SCI were identified via machine learning. The association between LRDEGs and inflammatory response in SCI mediated by immune cell infiltration was confirmed using Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Rats with subacute thoracic SCI were transplanted with hUC-MSCs, and transcriptome analyses were conducted on their spinal cords and retrieved hUC-MSCs, respectively.

The study identified 808 DEGs and 13 differentially infiltrated immune cell types in SCI patients compared to healthy controls. Multiple inflammatory response-related signaling pathways were activated in SCI. Seven LRDEGs, including LSP1, XRCC4, HSDL2, HNRNPH1, RPL14, IKZF1, and TP53, were recognized as key regulators. These genes are linked to immune cell infiltration and inflammatory responses in SCI. In SCI rats, the increased expression of LRDEGs and inflammatory cytokines were observed, which were significantly reduced after hUC-MSC transplantation. Differences in LRDEG expression patterns, enriched functions, and pathways between two SCI subtypes were statistically significant.

LRDEGs are involved in immune cell-mediated inflammatory response in SCI, and hUC-MSC transplantation reduces LRDEGs expression and inflammation response in the SCI microenvironment.

Colorectal cancer (CRC) is a highly prevalent cancer worldwide, but treatment outcomes can vary significantly among patients with similar clinical or historical stages. This study aimed to investigate the differences in immune cell abundance associated with malignant progression in CRC patients. We utilized data from patients with CRC obtained from The Cancer Genome Atlas as our training set. To assess immune cell infiltration levels, an immune cell risk score (ICRS) was calculated. Furthermore, we performed network analysis to identify effective T cell-related genes (ETRGs) and subsequently constructed an effective T cell prognostic index (ETPI). The performance of the ETPI was evaluated through external validation using four Gene Expression Omnibus datasets. Additionally, a nomogram analysis and drug sensitivity analysis were conducted to explore the clinical utility of the ETRGs. We also examined the expression of ETRGs in clinical samples. Based on the ICRS, we identified activated CD4+ and CD8+ T cells as protective factors in terms of prognosis. Six ETRGs were identified to develop the ETPI, which exhibited remarkable prognostic performance. In the external validation of immunotherapy, the low ETPI group demonstrated a significantly lower recurrence rate. To optimize therapeutic strategies, we developed a nomogram. Notably, patients with different ETPI values exhibited varying responses to tumor pathway inhibitors. Finally, we observed higher protein expression of certain ETRGs in normal tissues compared to tumors. Our findings suggest that the ETPI may contribute to the precise selection of patients based on tumor microenvironment and key genomic landscape interactions, thereby optimizing drug benefits and informing clinical strategies in future.

The incidence of heart failure with preserved ejection fraction (HFpEF) increases with the ageing of populations. This study aimed to explore ageing-associated gene signatures in HFpEF to develop new diagnostic biomarkers and provide new insights into the underlying mechanisms of HFpEF. Mice were subjected to a high-fat diet combined with L-NG-nitroarginine methyl ester (l-NAME) to induce HFpEF, and next-generation sequencing was performed with HFpEF hearts. Additionally, separate datasets were acquired from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were used to identify ageing-related DEGs. Support vector machine, random forest, and least absolute shrinkage and selection operator algorithms were employed to identify potential diagnostic genes from ageing-related DEGs. The diagnostic value was assessed using a nomogram and receiver operating characteristic curve. The gene and related protein expression were verified by reverse transcription PCR and western blotting. The immune cell infiltration in hearts was analysed using the single-sample gene-set enrichment analysis algorithm. The results showed that the merged HFpEF datasets comprised 103 genes, of which 15 ageing-related DEGs were further screened in. The ageing-related DEGs were primarily associated with immune and metabolism regulation. AGTR1a, NR3C1, and PRKAB1 were selected for nomogram construction and machine learning-based diagnostic value, displaying strong diagnostic potential. Additionally, ageing scores were established based on nine key DEGs, revealing noteworthy differences in immune cell infiltration across HFpEF subtypes. In summary, those results highlight the significance of immune dysfunction in HFpEF. Furthermore, ageing-related DEGs might serve as promising prognostic and predictive biomarkers for HFpEF.

This study investigates how TCN1 regulates inflammation and the cell cycle in psoriasis, focusing on the NF-κB pathway through in vitro experiments and bioinformatics analyses.

DEGs were identified by analyzing transcriptome data from four datasets comparing psoriatic lesions and normal skin (GSE34248, GSE30999, GSE14905, and GSE13355). Validation of TCN1 expression following biologic treatment was conducted using GSE201827, GSE51440, and GSE117239. GO and GSEA were performed to explore biological pathways. The expression levels of TCN1 in psoriatic lesions and healthy skin were assessed by qPCR and immunohistochemistry (IHC). In vitro, HaCaT keratinocytes were stimulated with TNF-α and IL-17 A, and TCN1 expression was modulated through siRNA-mediated knockdown and plasmid-mediated overexpression. Subsequent changes in TCN1 and key inflammatory cytokines were evaluated by qPCR and Western blotting (WB). Furthermore, immunofluorescence assays were performed to visualize the subcellular localization of TCN1 and the nuclear translocation of phosphorylated p65 (p-p65) in HaCaT cells. Cell cycle progression was assessed using BrdU-PI flow cytometry.

TCN1 was upregulated in psoriatic lesions, and its expression levels were positively correlated with the PASI score. Following biologic treatment, TCN1 expression was reduced. TCN1 overexpression was associated with activation of the NF-κB signaling pathway, accompanied by increased synthesis of psoriasis-related inflammatory mediators, as well as an elevated proportion of cells in the S phase of the cell cycle.

TCN1 is essential in modulating inflammation and the cell cycle in psoriasis, implying its value as both a biomarker for diagnosis and a candidate for therapeutic intervention.

Cancer development is driven by genetic alterations, particularly cancer driver mutations (CDMs), which are associated with aggressive phenotypes and shorter survival. In contrast, higher mutation loads caused by microsatellite instability (MSI) or mismatch repair deficiency (MMRd) can induce anti-cancer immunity, leading to tumor shrinkage and improved responses to immune checkpoint inhibitor (ICI) therapies. However, understanding how CDMs and MSI/MMRd influence cancer evolution remains limited. We opted uterine corpus endometrial carcinoma (UCEC) as a model in this study due to its MSI-high/MMRd characteristics. Somatic mutation screening revealed that UCEC has a significantly higher mutation rate in cancer driver genes compared to ovarian cancer (OVCA) and cervical squamous cell carcinoma (CSCC), despite these cancers arising from histologically connected organs in the reproductive tract. Interestingly, these CDMs did not necessarily drive tumor progression. Using a cutoff of 7.0 (mutations/Mb) for tumor mutation burden (TMB), we classified UCEC patients into two groups with distinct clinical features, genetic profiles, and drug sensitivities. Among the known CDMs, TP53 mutations and their functional networks emerged as key drivers in UCEC progression, while mutations in CTNNB1, PTEN, and ARID1A may enhance anti-tumor immunity, correlating with longer overall survivals. Drug screening using GDSC and CTRPv2 databases suggested that GSK-3 inhibitor IX may be effective for treating aggressive UCEC patients with a non-MSI phenotype. Curcumin showed efficacy for UCEC patients with MSI, especially with ICI therapy. Our study highlights the importance of immune regulation and tolerance over CDMs in cancer development, particularly in those with an MSI-high/MMRd phenotype. We propose that TMB could serve as a valuable screening method alongside molecular and histopathological classifications to guide treatment strategies for UCEC patients.

Diffuse large B-cell lymphoma (DLBCL), known as the predominant type of aggressive B-cell lymphoma, is biologically and clinically heterogeneous. The prognosis of DLBCL is quite different among subtypes. Hypoxia is one of the key elements in tumor microenvironment, promoting tumor progression by means of various mechanisms, such as increased proliferation, altered metabolism, enhanced angiogenesis, and greater migratory capability, among others. The primary purpose of this research is to investigate the connection between hypoxia-featured genes (HFGs), prognosis in DLBCL, and their capacity association with the immune microenvironment. Various hypoxia-associated patterns for DLBCL patients from GEO and TCGA databases were identified by means of an unsupervised consensus clustering algorithm. CIBERSORT and IOBR package is used to identify different immune infiltration status. To develop a predictive model using hypoxia-related genes, we conducted univariate Cox regression, multivariate Cox regression, and LASSO regression assessment. Subsequently, we confirmed the predictive importance of these hypoxia-associated genes, highlighting hypoxia-associated characteristics, and explored the connection between the hypoxia model and the immune environment. Three hypoxia clusters were identified. We also observed that each pattern of hypoxia response was significantly related to different prognoses. It was found that the immune status among hypoxia clusters is different. After developing a prognostic risk model using 5 hypoxia-related genes, we discovered that the risk score is related to immune factors and how effective drugs are in treating DLBCL. In DLBCL patients, varying hypoxia patterns correlate with both prognostic outcomes and the immune microenvironment. Hypoxia-featured genes (HFGs) function as a standalone predictive element in these patients. It is also potentially a reliable indicator for predicting clinical responses to ICI therapy and traditional drugs.

This study aimed to explore the molecular characteristics of neutrophil extracellular traps (NETs) in chronic rhinosinusitis with nasal polyps (CRSwNP). Differentially expressed gene analysis, weighted gene co-expression network analysis, and machine learning algorithms identified three core NETs-associated genes: CXCR4, CYBB, and PTAFR, which were significantly upregulated in CRSwNP patients. The diagnostic performance of these genes was evaluated using receiver operating characteristic (ROC) curves, and their clinical relevance was validated using multicenter data. Immune infiltration analysis showed strong correlations between these genes and neutrophil and immune cell infiltration. Single-cell RNA sequencing demonstrated that these genes were predominantly expressed in myeloid and immune cells and exhibited dynamic changes during disease progression. These genes may contribute to CRSwNP pathogenesis through IL-17 signaling and metabolism-related pathways. This study identifies novel biomarkers and therapeutic targets for precise diagnosis and personalized treatment of CRSwNP.

Cuproptosis is a brand-new copper-dependent type of cell death that has been linked to various tumors. However, the relationship between cuproptosis and acute lymphoblastic leukemia (ALL) remains to be further elaborated.

In ALL, 12 cuproptosis-related genes (CRGs) were analyzed at genetic and single-cell levels. Two molecular clusters were identified using "ConsensusClusterPlus". With the least absolute shrinkage and selection operator, a prognostic signature was built based on cuproptosis. The prognosis, clinical parameters, biological function, immune cell infiltration, therapy sensitivities, and transcription factor regulation of the clusters and risk subsets were further compared. Kaplan Meier curves, time-ROC curves, and nomogram were employed to evaluate the accuracy of the signature. Lastly, qRT-PCR was used to detect prognostic genes in cell lines and clinical samples.

CRGs exhibited extensive genetic variations and heterogeneous expression profiles in ALL. Single-cell analysis demonstrated that CRGs were strongly correlated with the biological characteristics of cancer cells. Two clusters and risk subgroups with distinct clinicopathological features, prognoses, biological functions, and drug sensitivities were identified. The cuproptosis signature was crucial in characterizing tumor immune landscape and cancer cell self-renewal ability. Furthermore, we explored that subtype A and high-scoring groups were more sensitive to immunotherapy. Multiple drugs with higher sensitivity among high-risk subgroups have been predicted. Nomograms demonstrated the clinical applicability of cuproptosis in risk assessment. The model was further validated in the verification cohort, our clinical specimens, and cell lines.

The cuproptosis-based model can characterize the tumor microenvironment, forecast survival results, and aid in improving risk assessment and personalized therapy options in ALL.

Soft tissue sarcomas (STS) imposes a substantial healthcare burden on society. The progression of these tumors is significantly influenced by diverse modes of programmed cell death (PCD), which can serve as valuable indicators for assessing prognosis and immune therapeutic response in STS. Nonetheless, the precise role of multiple cell death patterns in STS is yet to be clarified. We employed 96 machine-learning algorithm combination frameworks to identify novel cell death-related signatures (CDSigs) with the highest mean c-index, indicating their excellence. The independence test and comparison with previously published models further confirmed the stability and quality of these signatures for survival prediction in STS. The nomogram, comprising the cell death score (CDS) and clinical features, exhibited excellent predictive performance. Additionally, the CDSigs revealed associations with immune checkpoint genes and the immune microenvironment in STS. Furthermore, the results demonstrated that patients with lower CDS had the potential for greater benefit from immune therapeutic responses compared to those with higher CDS. Moreover, STS patients with low-risk scores exhibited heightened sensitivity to doxorubicin, axitinib, cisplatin, and camptothecin. Finally, the RT-qPCR results underscored significant differences in expression levels of several CDSigs genes between STS and normal cells. Overall, we comprehensively analyzed the multiple PCD in STS and established a novel CDSig for STS patients. This novel CDSig holds great promise in deciphering the prognosis, immune, and immune therapeutic response of STS.

Most patients with diffuse large B-cell lymphoma (DLBCL) treated with immunotherapies such as bispecific antibodies (BsAbs) or chimeric antigen receptor (CAR) T cells fail to achieve durable treatment responses, underscoring the need for a deeper understanding of mechanisms that regulate the immune environment and response to treatment. Here, an integrative multiomics approach was applied to multiple large independent data sets to characterize DLBCL immune environments and to define their association with tumor cell-intrinsic genomic alterations and outcomes to CD19-directed CAR T-cell and CD20 × CD3 BsAb therapies. This approach effectively segregated DLBCLs into 4 immune quadrants (IQs) defined by cell-of-origin and immune-related gene set expression scores. These quadrants consisted of activated B cell-like (ABC) hot, ABC cold, germinal center B cell-like (GCB) hot, and GCB cold DLBCLs. Recurrent genomic alterations were enriched in each IQ, suggesting that lymphoma cell-intrinsic alterations contribute significantly to orchestrating unique DLBCL immune environments. For instance, SOCS1 loss-of-function mutations were significantly enriched among GCB hot DLBCLs, identifying a putative subset of inflamed DLBCLs that may be inherently susceptible to immunotherapy. In patients with relapsed/refractory DLBCL, DLBCL-IQ assignment correlated significantly with clinical benefit with a CD20 × CD3 BsAb (N = 74), but not with CD19-directed CAR T cells (Stanford, N = 51; Memorial Sloan Kettering Cancer Center, N = 69). Thus, DLBCL-IQ provides a new framework to conceptualize the DLBCL immune landscape and suggests the endogenous immune environment has a more significant impact on outcomes to BsAb than CAR T-cell treatment.

The role of RAP1GAP in tumor progression has garnered increasing attention; however, its prognostic value and immunological influence across various cancers remain uncertain. Our study presents a pan-cancer analysis to investigate its involvement in oncogenesis and immune regulation.

Public databases were utilized to assess RAP1GAP expression across cancers. Cox regression analysis evaluated its prognostic value, while Pearson correlation examined associations with genomic heterogeneity, tumor stemness, immune cell infiltration, and immune checkpoints. Immunohistochemical staining of bladder cancer and adjacent tissues assessed RAP1GAP expression and clinical correlations.

RAP1GAP expression is differentially expressed in a variety of tumor types and predicts a better or worse prognosis for tumor patients. It was strongly linked to genomic heterogeneity and tumor stemness in multiple cancers. Immunohistochemistry showed increased RAP1GAP expression in bladder cancer. Immune cell analysis revealed high RAP1GAP expression was associated with greater infiltration of plasma cells, naive CD4 + T cells, Tregs, and eosinophils, while low expression correlated with increased CD8 + T cells, activated memory CD4 + T cells, and M1 macrophages.

RAP1GAP is a potential prognostic biomarker and immune regulator, with promising implications as an immunotherapeutic target for bladder cancer.

Clear cell renal cell carcinoma (ccRCC) is distinguished by metabolic irregularities and unique immunological profiles. Nevertheless, the comprehensive examination of immune and metabolic attributes within the tumor microenvironment of ccRCC remains inadequately elucidated. In this study, we identified two distinct molecular subtypes (C1 and C2) of ccRCC using the non-negative matrix factorization (NMF) algorithm. Utilizing univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we developed a prognostic signature comprising eight immune- and metabolism-related genes (IMRGs) associated with the tumor microenvironment. The validation of this signature was performed using both testing and entire datasets. A nomogram was developed using IMRGs prognostic signature and various clinical parameters, including age and TNM stage. We also performed the in vitro experiments to validate the carcinogenic role of PYCR1 in ccRCC cells. Subtype C1 exhibited a more favorable prognosis and higher levels of immune cell infiltration compared to subtype C2. The AUCs of the nomogram at 1-, 3-, and 5-year intervals (AUC = 0.874, 0.820, and 0.794) were slightly higher than those of the IMRGs signature alone (AUC = 0.773, 0.755, and 0.764). The association between risk score and immune checkpoint expressions, immunophenoscore (IPS), and microsatellite instability (MSI) collectively predicted treatment efficacy accurately. Additionally, in vitro experiments confirmed the involvement of PYCR1 in promoting the aggressive behaviors of ccRCC cells, as evidenced by reduced proliferation, invasion, and enhanced apoptosis upon PYCR1 knockdown. In conclusion, the IMRGs signature shows promise in predicting prognostic risk, assessing the effectiveness of immunotherapy, and tailoring treatment for ccRCC patients.

Gastric cancer (GC) has a high incidence and poor prognosis, often metastasizing to the liver. Gamma-glutamyl transferase (GGT) is a key indicator of liver damage, and its family members are associated with various cancers. However, their expression and prognostic significance in GC remain unclear. This study utilized R to analyze the expression and prognosis of GGT family members using RNA-seq and clinical data from the TCGA database, applying Lasso regression for key gene identification. We identified GGTLC2 as a significant gene related to GC prognosis. We examined the clinical relevance, methylation levels, and copy number variations of GGTLC2 using the MEXPRESS database and performed GSEA analysis to identify enriched pathways. Additionally, we explored the relationship between GGTLC2 and immune cell infiltration, as well as immune-related genes, and established GGTLC2 knockdown and overexpression cell lines. The results indicate that GGTLC2 is highly expressed in GC and is associated with DNA methylation, copy number variation, and liver metastasis. Functionally, GGTLC2 is primarily enriched in oxidative stress and immune-related pathways, affecting immune infiltration and the expression of inflammatory factors in the tumor microenvironment. In vivo and in vitro studies demonstrate that knocking down GGTLC2 inhibits GC proliferation, invasion, and migration while promoting apoptosis and ferroptosis. Conversely, overexpressing GGTLC2 significantly increases the number of metastatic tumors in the liver. Overall, GGTLC2 may influence the occurrence, development, and liver metastasis of GC by inhibiting ferroptosis, making it a promising novel biomarker for the diagnosis and treatment of GC and its metastasis.

Gastric cancer (GC) ranks as the fifth most prevalent malignant tumor and stands as the fourth leading contributor to cancer-related fatalities on a global scale. The specific link between CD2 Associated Protein (CD2AP) expression and the tumor microenvironment (TME) remains unclear, and further exploration is needed to understand its potential role in immune response and as a target for immunotherapy in GC. Utilizing RNA sequencing data acquired from The Cancer Genome Atlas (TCGA) for a pan-cancer analysis, a comprehensive evaluation was carried out to determine the expression pattern and immunological involvement of CD2AP. Systematic association of CD2AP with immunological features within the stomach adenocarcinoma (STAD) TME was subsequently performed, encompassing factors like cancer immunity cycles, immune checkpoints, immunomodulators, tumor-infiltrating immune cells (TIICs). We found that CD2AP was enhanced expression in the TME of a variety of malignancies. CD2AP contributes to forming a stromal reduced TME in GC and improve the efficacy of immunotherapy. It was observed that patients with elevated levels of CD2AP, along with high scores on their CD4, CD20, and CD57 immune markers, tended to experience the most favorable prognosis. Furthermore, an IRS was constructed to accurately assess the prognosis of STAD patients. Since CD2AP was associated with the formation of stromal reduced TME in STAD, the expression of CD2AP can improve the effect of immunotherapy of STAD. CD2AP could emerge as a novel prognostic biomarker for STAD, offering a fresh avenue for molecular targeted therapy.

Ovarian cancer (OC), a common fatal malignancy in women, has a poor prognosis. RNA modifications are associated with the development of OC. In this study, we aimed to identify and verify RNA modifications-related prognostic genes in OC by integrating bulk and single-cell RNA sequencing (scRNA-seq) data.

Transcriptome data came from public databases and RNA modifications-related genes (RMRGs) were obtained from literature. Candidate genes were identified by intersecting RMRGs with differentially expressed genes (DEGs) in OC patients. Prognostic genes were gained via machine learning techniques, particularly LASSO regression. A risk model was built to predict the prognosis. OC patients were divided into high-risk and low-risk groups according to risk score. Subsequent analyses covered enrichment analysis, immune microenvironment, mutation analysis, and chemotherapeutic drug sensitivity. In addition, scRNA-seq data was assessed for key cells and gene expression in them. Finally, RT-qPCR was applied to identify the expression of prognostic genes.

LSM4, SNRPC, ZC3H13, LSM2, WTAP, DCP2, PUS7, and TUT1 were selected as prognostic genes. The risk model exhibited excellent predictive abilities. Seventeen pathways were enriched like calcium signaling pathway, 7 differential immune cells were identified like regulatory T cells and plasmacytoid dendritic cells, and TP53 had highest mutation rate. Half-maximal inhibitory concentrations (IC50) values of 47 drugs like paclitaxel differed between two risk groups. The prognostic genes were distributed mainly in fibroblast cells, epithelial cells and endothelial cells. During fibroblast cells differentiation, expression of prognostic genes fluctuated to varying degrees. The RT-qPCR demonstrated that the expression of LSM2, LSM4, PUS7, SNRPC, and TUT1 were upregulated in OC, while DCP2, WTAP, and ZC3H13 were downregulated.

We constructed an RNA modifications-related prognostic signature that can effectively predict clinical outcomes and therapeutic responses in patients with OC.

We have recently described that the most prevalent 100 mutations identified in human cancers, both single nucleotide variations (SNVs) and InDels, generate a handful number of shared mutated neoantigens (SNV and InDel-NeoAgs) in association with 5 HLA-A and 7 B haplotypes.

In the present study, we expanded such analysis to 50 haplotypes in the three MHC class I loci (10 HLA-A, 27 HLA-B and 13 HLA-C), including all the mutated proteins identified in at least 5% of cancer patients.

Overall, the extended analysis identified 15 SNV-NeoAgs and 55 InDel-NeoAgs with a significant affinity improvement over the corresponding wt (DAI > 10). These targetable shared NeoAgs are prevalently derived from PIK3CAH1047R (6/15 SNV-NeoAgs) and LARP4BT163Hfs (30/55 InDel-NeoAgs). From the HLA perspective, the HLA-A*33:03 is associated with the largest number of SNV-NeoAgs (4/15 NeoAgs) and the HLA-B*58:01 is associated with the largest number of InDel-NeoAgs (16/55 NeoAgs). According to the distribution of each HLA haplotype in at least 10% of the regional populations, therapeutic cancer vaccines based on mutated shared SNV and InDel-NeoAgs, might be developed for COAD, STAD and UCEC cancers, with a global coverage, and for PAAD and UVM, with a regional coverage.

This represents the first in-depth analysis for the identification of a specific repertoire of shared mutated NeoAgs, most of which never reported before. Such shared SNV and InDel-NeoAgs are indispensable for the development of "off-the-shelf" cancer vaccines targeting a relevant percentage of cancers in a significant percentage of cancer patients worldwide.

Lung adenocarcinoma (LUAD) is one of the leading causes of death worldwide, and thus, more biomarker and therapeutic targets need to be explored. Herein, we aimed to explore new biomarkers of LUAD by integrating bioinformatics analysis with cell experiments. We firstly identified 266 druggable genes that were significantly differentially expressed between LUAD tissues and adjacent normal lung tissues. Among these genes, SMR analysis with p-value correction suggested that declining lipoprotein lipase (LPL) levels may be causally associated with an elevated risk of LUAD, which was corroborated by co-localization analysis. Analyses of clinical data showed that LPL in lung cancer tissues has considerable diagnostic value for LUAD, and elevated LPL levels were positively associated with improved patient survival outcomes. Cell experiments with an LPL activator proved these findings; the activator inhibited the proliferation and migration of lung cancer cells. Next, we found that LPL promoted the infiltration of immune cells such as DCs, IDCs, and macrophages in LUAD by mononuclear sequencing analysis and TIMER2.0. Meanwhile, patients with low levels of LPL expression demonstrated superior immunotherapeutic responses to anti-PD-1 therapy. We conclude that LPL acts as a diagnostic and prognostic marker for LUAD.

A new perspective on cancer metabolism suggests that it varies by context and is diverse. Cancer metabolism reprogramming can create a heterogeneous microenvironment that affects immune cell infiltration and function, complicating the selection of treatment methods. However, the specifics of this relationship remain unclear in breast cancer. This research aims to explore how glycolysis and fatty acid metabolism (GF) influence the immune microenvironment and their predictive capabilities for immunotherapy responses and overall survival.

We at first time identified 602 GF-related genes. Utilizing multiple datasets from various centers and employing 10 different machine learning algorithms, we developed a GF-related signature called GFSscore, driven by artificial intelligence.

The GFSscore served as an independent prognostic indicator and demonstrated greater robustness than other models. Its validity was validated through multiple databases. Our study found that breast cancer patients with a high GFSscore, indicative of a greater tendency towards glycolytic activity, experienced poorer prognosis due to immunosuppression from distinct immune evasion mechanisms. Conversely, those with a low GFSscore, more inclined towards fatty acid metabolism, had better outcomes. Additionally, the GFSscore has the potential to forecast how well a patient might respond to immunotherapy and their susceptibility to chemotherapy medications. Moreover, we found that the overexpressed ACSL5 gene inhibits the proliferation of BRCA through experiments.

The GFSscore may offer patients personalized therapy by identifying new therapeutic targets for tumors. By understanding the relationship between cancer metabolism and the immune microenvironment, we can better tailor treatments to individual patients.

Colorectal cancer (CRC) remains a major cause of cancer mortality, and dysregulated glutamine metabolism has emerged as a potential therapeutic target. However, the precise role of glutamine in CRC progression and treatment response remains debated.

The authors collected transcriptome and microbiome information, from multiple sources to construct the GLMscore, a prognostic signature in CRC. To comprehensively characterize the biological features of GLMscore groups, the integration of transcriptomic profiling, KEGG pathway enrichment analysis, immune infiltration analysis, tumor immune microenvironment characterization, microbiome analysis, and tissue imaging were applied. Furthermore, CRC patients were stratified into GLMscore high and GLMscore low groups. The robustness of GLMscore was validated in both training and validation cohorts, and the predictive value for immunotherapy response was assessed. Finally, single-cell RNA sequencing (scRNA-seq) analysis was conducted to delineate the differences between GLMscore high and GLMscore low groups.

High GLMscore was associated with elevated expression of pathways related to tumorigenesis, epithelial-mesenchymal transition (EMT), and angiogenesis. Furthermore, high GLMscore patients exhibited an immunosuppressive TME characterized by increased infiltration of M0 and M2 macrophages, reduced overall immune infiltration (supported by ESTIMATE and TIDE scores), and increased expression of immune exclusion and suppression pathways. Analysis of pathological whole-slide images (WSIs) revealed a lack of intratumoral tertiary lymphoid structures (TLSs) in high GLMscore patients. The GLMscore also predicted resistance to common chemotherapeutic agents (using GDSC data) and, importantly, predicted poor response to immunotherapy in the IMvigor210 cohort. Analysis of 16S rRNA gene sequencing data revealed an enrichment of potentially oncogenic microbiota, including Hungatella and Selenomonas, in high GLMscore group. Single-cell analysis further confirmed the immunosuppressive TME and identified increased cell-cell communication between inflammatory macrophages and tumor cells in high GLMscore group.

The authors innovatively constructed GLMscore, a robust scoring system in quantifying CRC patients, exploring the distinct biological features, tumor immune microenvironment and microbiome ecology, exhibiting high validity in predicting survival prognosis and clinical treatment efficacy.

Solute carrier (SLC) proteins are essential for nutrient transport, influencing tumor metabolism and growth while preserving cellular homeostasis. Despite the critical biological functions of these transporters, their applicability as therapeutic targets in clear cell renal cell carcinoma (ccRCC) remains largely unexplored. In the current study, we analyzed transcriptomic data and discovered 77 differentially expressed SLC genes in ccRCC, with 24 demonstrating predictive potential. Using Lasso regression, we developed a prognostic signature comprising six key genes: SLC2A3, SLC11A1, SLC14A1, SLC16A8, SLC22A6, and SLC28A1. This signature demonstrated strong diagnostic performance and served as an independent predictor of patient survival. Further analysis integrating clinical variables and risk scores enabled the construction of nomograms, which exhibited high predictive accuracy for patient outcomes. Immune profiling revealed distinct infiltration patterns between risk groups: high-risk patients showed elevated levels of memory B cells, activated CD4+ T cells, regulatory T cells (Tregs), M0 macrophages, and neutrophils. In contrast, their low-risk counterparts showed M1 macrophages, resting dendritic cells, and resting mast cells. Validation experiments confirmed that SLC16A8 was significantly overexpressed in ccRCC tissues compared to normal samples, correlating with poor prognosis. Functional studies demonstrated that SLC16A8 knockdown impaired tumor progression in vitro. Consistent with these findings, in vivo experiments demonstrated reduced tumor growth upon SLC16A8 knockdown. Mechanistically, decreased SLC16A8 attenuated PI3K/AKT signaling, suggesting a potential regulatory pathway in ccRCC progression. In summary, we established a six-gene SLC signature with significant prognostic value in ccRCC. Among these genes, SLC16A8 emerged as a promising biomarker and therapeutic target, warranting further investigation.

Lung adenocarcinoma (LUAD) is one of the most aggressive and rapidly lethal tumor types. Previous studies have demonstrated the involvement of antidiuretic hormone (ADH)-related genes in cancer. However, the role of ADH-related genes in LUAD remains unclear. Therefore, investigating the characteristics of these genes in LUAD is essential.

Differentially expressed genes (DEGs) associated with ADH in LUAD were identified using the STRING database. Consensus clustering was performed, and a protein-protein interaction network was constructed for the DEGs between subtypes. Genes extracted from the PPI network underwent univariate, LASSO, and multivariate Cox regression analyses to develop a predictive model for LUAD. A nomogram integrating clinical data and risk scores was created, and its prognostic power for overall survival (OS) in LUAD patients was evaluated. Additionally, LUAD patients were analyzed for targeted therapies, immune landscape, functional enrichment, and mutation profiles. Finally, qRT-PCR was used to examine the expression of signature genes in LUAD cells.

Based on ADH-related DEGs, LUAD patients were stratified into two clusters (Cluster 1 and Cluster 2) with distinct survival outcomes. A predictive model incorporating nine feature genes was subsequently constructed using DEGs from these two subtypes. The receiver operating characteristic curve demonstrated the model's prognostic accuracy in predicting OS in LUAD patients. Compared to the high-risk group, patients in the low-risk group exhibited higher immune infiltration levels and immunophenoscore, along with lower tumor immune dysfunction and exclusion scores. Enrichment analysis revealed that immune response pathways and ligand-receptor interactions were the primary functional categories distinguishing the high- and low-risk groups. The low-risk group showed a significantly lower gene mutation burden. Drug sensitivity analysis identified several potential targeted therapies, including Dabrafenib, ARQ-680, Vemurafenib, BGB-283, MLN-2480, and GDC-0994, which might act on hub genes. qRT-PCR validation confirmed that DNAH12 was significantly downregulated in tumor tissues, while DKK1, DLX2, IGFBP1, NTSR1, RPE65, and VGF were markedly upregulated.

This study provided potential prognostic biomarkers for LUAD and might facilitate the development of effective immunotherapy strategies for LUAD patients.

Diabetic retinopathy (DR) is a significant complication of diabetes, with complex pathogenesis involving epigenetic modifications. This study aimed to explore the epigenetic regulatory mechanisms contributing to DR.

DR-related data, including DNA methylation, mRNA, and miRNA expression datasets, were obtained from the Gene Expression Omnibus database. Differential gene expression analysis was performed to identify differentially methylated genes and expressed mRNAs and miRNAs. Cross-analysis established the methylation-expression and miRNA-mRNA regulatory networks. A comprehensive DR-related epigenetic regulatory network was constructed, identifying hub genes. The expression characteristics of these hub genes in various immune cells were examined using single-cell RNA sequencing.

We identified 10,716 differentially methylated genes, 1,181 differentially expressed mRNAs, and 615 differentially expressed miRNAs in DR. The methylation-expression regulatory network was associated with pathways such as TGF-beta and ErbB signaling. The miRNA regulatory network was linked to pathways related to cellular senescence, adherents junctions, and endocytosis. Five hub genes were identified: TFRC, AP2M1, AP2A1, DAB2, and PPP1CB. Single-cell RNA sequencing revealed specific expression of these genes in particular immune cells, highlighting their potential roles in DR pathogenesis.

This study constructed a comprehensive epigenetic regulatory network for DR and identified key regulatory genes, offering new insights into the molecular mechanisms underlying DR and potential therapeutic targets for diagnosis and treatment.

Unrepaired DNA damage is the initiation of mutation and tumor-specific biological characteristics. Oxidative stress and base excision repair (BER) are the two main pathways to cope with oxidative DNA damage, which is closely related to the heterogeneity of Lung adenocarcinoma (LUAD), but their relationship with tumor biological characteristics is unclear, and a molecular subtyping based on comprehensive BER and oxidative stress gene expression is lacking. 501 samples from The Cancer Genome Atlas (TCGA) were classified into three subtypes based on genes related to BER and oxidative stress through hierarchical agglomerative cluster analysis. By integrating the nearest template prediction (NTP), four GEO datasets and 52 samples from our institution were analyzed for validation. Bioinformatic analysis was performed to define the diverse molecular characteristics, mutation background, tumor microenvironment, and prognosis. Three subtypes with distinct gene signatures were identified: relatively high BER and low oxidative stress gene expression (C1), low BER gene and high oxidative stress gene expression (C2), and high expression of both BER and oxidative stress genes (C3). C2 was characterized by a low mutation frequency in TP53 (29%) and a high mutation frequency in EGFR (20%), whereas a high frequency of mutation was seen in C3 in STK11 and KEAP1 genes. Additionally, differentially expressed genes among the three subtypes were particularly enriched in immune-related pathways, and the abundance of immune cells and Immunophenoscore were significantly higher in C2, while the Tumor Immune Dysfunction and Exclusion (TIDE) score was lower in C2, indicating a better response to immunotherapy. C2 was also associated with an improved survival outcome compared with C1 and C3, and this finding was validated in 978 samples from four independent GEO datasets and 52 samples at our institution by the NTP algorithm. The three-subtype classifications based on BER and oxidative stress gene expression offers potential for predicting the survival and response to immunotherapy of LUAD patients.

Ovarian cancer ranks as the fifth most common cause of cancer-related deaths in women worldwide. Macrophages M2 is believed to support tumor growth by suppressing immune responses and promoting angiogenesis.

A macrophage M2-related signature (MRS) was developed by applying machine learning methods that included 10 algorithms and utilized data from the TCGA, GSE14764 and GSE140082 datasets. The predictive capacity of the MRS for immunotherapy response was evaluated through various methods, including immunophenoscore, TIDE score, TMB score, immune escape score, as well as two immunotherapy cohorts (IMvigor210 and GSE91061).

The optimal MRS, developed using the lasso algorithm, served as an independent prognostic factor and demonstrated stable performance in predicting overall survival rates in ovarian cancer. In the TCGA dataset, the AUC values for the 1-, 3-, and 5-year ROC curves were 0.874, 0.808, and 0.813, respectively. The C-index of our MRS was superior to that of clinical stage, tumor grade, and several other established prognostic signatures. Ovarian cancer patients with low risk score exhibited higher ESTIMATE score, increased levels of immune cells, elevated PDI&CTLA4 immunophenoscore, higher TMB score, reduced TIDE score, and lower immune escape score. Additionally, the survival prediction nomogram displayed significant potential for clinical application in estimating the 1-, 3-, and 5-year overall survival rates of ovarian cancer patients.

Our study developed a novel MRS for ovarian cancer, which could act as an indicator for predicting the prognosis and immunotherapy response in ovarian cancer.

Epigallocatechin gallate (EGCG), a frequently studied catechin in green tea, has been shown to be involved in the antiproliferation and apoptosis of human Nasopharyngeal carcinoma (NPC) cells. However, the pharmacological targets and mechanism by which EGCG can combat NPC patients remain to be studied in detail.

Network pharmacology and bioinformatics were employed to investigate the molecular mechanisms underlying EGCG's therapeutic effects on NPC, with an emphasis on developing a prognostic risk model and identifying potential therapeutic targets.

A novel prognostic risk model was developed using univariate Cox regression, LASSO regression and multivariable Cox regression analyses, incorporating six genes to stratify patients into low- and highrisk groups. Kaplan-Meier analysis demonstrated significantly shorter progression-free survival in the high-risk group. The model's accuracy was further validated using time-dependent Receiver Operating Characteristic (ROC) curves. ESTIMATE analysis revealed significantly higher immune, stromal and overall ESTIMATE scores in the low-risk group compared to the high-risk group. Immune profiling indicated significant differences in five immune cell subtypes (memory B cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells and activated dendritic cells) between the two risk groups. Additionally, the low-risk group showed greater sensitivity to conventional chemotherapeutic agents. Immunohistochemistry and molecular docking analyses identified CYCS and MYL12B as promising targets for EGCG treatment.

This study utilised network pharmacology and bioinformatics to identify shared genes between EGCG and NPC, aiming to elucidate the molecular mechanisms through which EGCG inhibits NPC and to develop a prognostic model for assessing patient outcomes. The findings provide potential insights for the development of anti-NPC therapies and their clinical applications.

Bladder cancer (BLCA) is the tenth most commonly diagnosed cancer and poses a significant challenge due to its complexity and associated high morbidity and mortality rates in the absence of optimal treatment. The tumor microenvironment (TME) is recognized as a critical factor in tumor initiation, progression and therapeutic response, and offers numerous potential targets for intervention. A comprehensive understanding of immune infiltration patterns in BLCA is essential for the development of effective prevention and treatment strategies. In this study, bioinformatics analysis was used to identify differentially expressed genes (DEGs) and tumor-infiltrating immune cells (TIICs) between BLCA tissues and adjacent normal tissues. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) analysis were used to identify the top 10 hub genes with the most significant co-expression effects, and their potential relationship with patient prognosis was then predicted. The random survival forest (RSF) model was used to further identify six variables among the hub genes and establish a novel scoring system, defined as the tumor-infiltrating immune score (TIIS) to predict the prognosis of BLCA patients. In addition, the correlation analysis between TIIS and drug sensitivity was investigated using the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) databases. Patients with high TIIS were found to have a poor prognosis but may be more sensitive to Cisplatin and certain novel agents. This study provided a systematic analysis of immune cell infiltration in BLCA and established TIIS to predict patient prognosis and the efficacy of specific drugs in the treatment of BLCA.

Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effective immunotherapy, this study was designed to screen molecular markers related to tumor infiltrating cells in LUAD, aiming to improve immunotherapy response during LUAD therapy.

The ConsensusClusterPlus method was used for clustering immune molecular subtypes of LUAD. Immune cell infiltration and immunotherapeutic potential in each subtype was evaluated employing single-sample gene set enrichment analysis (ssGSEA), Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS). Immune-related co-expression modules were classified by weighted gene co-expression network analysis (WGCNA) analysis. The sequencing data of immune-related genes were comprehensively analyzed by introducing a new computational framework and 10 machine learning algorithms (a total of 101 combinations) to determine the prognostic genes, which were further combined to develop an immune prognostic signature (IMMPS) using the stepCox and Ridge methods. The expression of the signature genes was validated by quantitative real-time PCR (qRT-PCR).

Samples from The Cancer Genome Atlas dataset (TCGA-LUAD) were divided into two subtypes (immunosuppressive subgroup C1 and immune-activated subgroup C2); notably, the C2 subgroup was more likely to benefit from immunotherapy (p < 0.05). An IMMPS developed based on seven immune infiltrating cell-related genes (SEMA7A, EFHD2, CHST11, SLC24A4, MAL, JCHAIN, and SCARF1) could accurately predict the overall survival of LUAD in five LUAD cohorts, with an average C-index higher than 0.69. LUAD patients with a low IMMPS value had a higher immune cell infiltration (p < 0.05). In addition, the IMMPS exhibited better prediction performance in comparison to 154 published gene signatures, suggesting that the IMMPS was an independent prognostic risk factor for evaluating the overall survival of LUAD patients. Since BTNL9 was the most relevant immune checkpoint gene, in vitro experiment showed that the expression of the seven key genes (SEMA7A, EFHD2, CHST11, SLC24A4, MAL, JCHAIN, and SCARF1) in LUAD cell lines was consistent with that in normal lung epithelial cells after inhibiting BTNL9 expression (p < 0.05).

Our results contributed to a better understanding of immunological characteristics of LUAD. The IMMPS could serve as a promising tool for improving the clinical outcome of patients suffering from LUAD.

Bladder cancer remains a major challenge in clinical oncology, particularly due to the development of platinum resistance, which severely impacts patient prognosis. Despite numerous attempts to create effective prognostic models, their clinical applicability has often been limited.

In this study, we utilized a robust statistical approach, LASSO-COX regression analysis, to develop a novel prognostic model for bladder cancer based on cisplatin sensitivity-related genes (CSRGs). The model was validated using both the TCGA-BLCA dataset and an independent validation set, GSE32894. Additionally, we employed various in vitro assays, including CCK-8 and EdU assays for cell proliferation, transwell assays for migration, and flow cytometry for apoptosis analysis, to investigate the biological function of the identified genes.

Our prognostic model demonstrated superior predictive performance, with high AUC values. SCAMP2 was identified as a critical gene with elevated expression in bladder cancer, showing strong correlation with sensitivity to multiple anti-cancer drugs, including cisplatin. Further functional assays revealed that SCAMP2 mediates drug resistance in bladder cancer cells via the NOTCH signaling pathway. Additionally, in vivo experiments showed that SCAMP2 overexpression significantly enhanced cisplatin sensitivity in bladder cancer tissues.

These findings underscore the potential of CSRGs, particularly SCAMP2, as critical biomarkers for bladder cancer prognosis. The identification of SCAMP2 as a regulator of NOTCH signaling in cisplatin resistance offers new insights into the molecular mechanisms of chemotherapy resistance and suggests potential therapeutic targets for overcoming drug resistance. Our model could guide personalized treatment strategies and improve bladder cancer patient outcomes.

Cancer-related deaths continue to rise, largely due to the suboptimal efficacy of current treatments. Fortunately, immunotherapy has emerged as a promising alternative, offering new hope for cancer patients. Among various immunotherapy approaches, targeting tumor-specific antigens (TSAs) has gained particular attention due to its demonstrated success in clinical settings. Despite these advancements, there are still gaps in our understanding of TSAs. Therefore, this review explores the life cycle of TSAs in cancer, the methods used to identify them, and recent advances in TSAs-targeted cancer therapies. Enhancing medical professionals' understanding of TSAs will help facilitate the development of more effective TSAs-based cancer treatments.

Oral squamous cell carcinoma (OSCC) is one of the most common malignancies with poor clinical outcome. Regulatory T cells (Tregs) have a dual role in maintaining immune homeostasis and suppressing anti-tumor immunity. The role of Tregs related genes (TRGs) in the prognosis of OSCC patients were rarely been reported.

Integrative analysis procedure containing 10 machine learning methods was used to develop a Tregs related gene signature (TRS) using datasets from TCGA, GSE41613, GSE65858, and GSE117973 cohort. Several predicting scores were used to investigate the performance of TRS in predicting immunotherapy benefit. The biological function of TNFAIP3 was explored by in vitro assay.

The prognostic signature constructed using the LASSO algorithm was identified as the optimal TRS with the highest average C-index of 0.78. TRS served as a prognostic biomarker, with low TRS score correlating with favorable clinical outcome. Specifically, in TCGA dataset, the 1-, 3-, and 5-year AUC values were 0.796, 0.838 and 0.812, respectively. OSCC with low TRS score exhibited higher levels of immune-activated cells and immune-related functions, including CD8+ T, macrophage M1, and cytolytic activity. Low TRS indicated higher PD1&CTLA4 immunophenoscores, higher TMB, higher MSI, lower tumor immune dysfunction and exclusion score, decreased immune escape score, and lower intratumor heterogeneity scores. Additionally, OSCC cases with high TRS score showed elevated cancer-related hallmark score. Knock-down of TNFAIP3 inhibited OSCC cell proliferation.

This research established an optimal TRS for OSCC, which functions as a valuable indicator for forecasting clinical outcomes and assessing potential benefits from immunotherapy.