Pancreatic cancer is a highly aggressive cancer characterized by low survival rate. Enhanced ribosome biogenesis may be associated with tumor drug resistance and malignant phenotypes, representing a potential therapeutic target in pancreatic cancer. Therefore, exploring the molecular mechanisms of ribosome biogenesis in pancreatic cancer may uncover new biomarkers and potential therapeutic targets, facilitating the development of personalized treatment strategies.

Ribosome biogenesis-related gene signatures were acquired from TCGA and Gene Cards databases. Prognostic gene sets were screened using machine learning algorithms to construct a risk model, which was externally validated via GEO database. Single-cell RNA sequencing analysis (GSE155698 dataset) was performed to assess gene expression patterns and module scores.

Sixty ribosome biogenesis-related prognostic genes were identified in pancreatic cancer. Cox regression and machine learning algorithms selected nine pivotal biomarkers (ECT2; CKB; HMGA2; TPX2; ERBB3; SLC2A1; KRT13; PRSS3; CRABP2) with high diagnostic and prognostic specificity for PAAD. The machine learning-derived risk score correlated strongly with tumor proliferation pathways and immunosuppression, suggesting dual roles in tumor promotion and immunosuppressive microenvironment remodeling. Single-cell analysis highlighted predominant expression of CKB, SLC2A1, ERBB3, CRABP2, and PRSS3 in pancreatic ductal epithelial cells.

Our results shed light on the potential connections between ribosome biogenesis-related molecular characteristics and clinical features, the tumor microenvironment, and clinical drug responses. The research underscores the critical role of ribosome biogenesis in the progression and treatment resistance of pancreatic cancer, offering valuable new perspectives for prognostic evaluation and therapeutic response prediction in pancreatic cancer.

Background/Objectives: To evaluate the association between endometrial tissue stiffness, as measured by shear wave elastography (SWE), and the presence of specific gene mutations in patients diagnosed with endometrial cancer. Methods: Peripheral blood samples were collected for DNA extraction and next-generation sequencing (NGS) to identify gene mutations. Preoperative SWE was performed to measure endometrial stiffness, with values expressed in kilopascals (kPa). Statistical analyses were conducted to assess the correlation between SWE measurements and genetic findings. Results: Genetic mutations were detected in 66% (n = 31) of cases, with TTN, PLEC, and PRSS1 being the most frequently mutated genes. The median SWE measurement was 36.5 kPa (range: 19.1-70.4 kPa). No statistically significant correlation was found between SWE values and the presence of gene mutations (p > 0.05). Cases with metastasis exhibited higher median SWE values (40.1 kPa) compared to non-metastatic cases (34.7 kPa), though this difference was not statistically significant. Conclusions: While no significant association was observed between endometrial stiffness and specific gene mutations, higher SWE values in metastatic cases suggest that increased tissue stiffness may be linked to tumor aggressiveness. Further large-scale studies are warranted to validate these findings and explore the potential of SWE as a non-invasive tool in assessing endometrial cancer characteristics.

Early-stage poorly differentiated lung adenocarcinoma (LUAD) is plagued by a high risk of postoperative recurrence, and its prognostic heterogeneity complicates treatment and surveillance planning. We conducted this integrative multi-omics study to identify those patients with a truly high risk of adverse outcomes.

Whole-exome, RNA and whole methylome sequencing were carried out on 101 treatment-naïve early-stage poorly differentiated LUADs. Integrated analyses were conducted to disclose molecular characteristics and explore molecular subtyping. Functional validation of key molecules was carried out through in vitro and in vivo experiments.

Recurrent tumors exhibited significantly higher ploidy (p = 0.024), the fraction of the genome altered (FGA, p = 0.042), and aneuploidy (p < 0.05) compared to non-recurrent tumors, as well as a higher frequency of CNVs. Additionally, recurrent tumors showed hypomethylation at both the global level and in CpG island regions. Integrative transcriptomic and methylation analyses identified three molecular subtypes (C1, C2, and C3), with the C1 subtype presenting the worst prognosis (p = 0.024). Although frequently mutated genes showed similar mutation frequencies across the three subtypes, the C1 subtype exhibited the highest tumor mutation burden (TMB), mutant-allele tumor heterogeneity (MATH), aneuploidy, and HLA loss of heterozygosity (HLA-LOH), along with relatively lower immune cell infiltration. Furthermore, GINS1 and CPT1C were found to promote LUAD progression, and their high expression correlated with a poor prognosis.

This multi-omics study identified three integrative subtypes with distinct prognostic implications, paving the way for more precise management and postoperative monitoring of early-stage poorly differentiated LUAD.

Gastric cancer (GC) remains a leading cause of cancer-related deaths and exhibits considerable heterogeneity among patients. Thus, accurate classifications are essential for predicting prognosis and developing personalized therapeutic strategies. To address this, we retrospectively analyzed multi-omics data from 359 GC samples, incorporating transcriptomic RNA (mRNA), DNA methylation, mutation data, and clinical parameters. Using ten clustering algorithms, we integrated these datasets to classify GC into molecular subtypes. The robustness of our clustering approach was externally validated using an independent cohort generated from different sequencing technologies, and we characterized the heterogeneity of each subtype. Our analysis identified three distinct molecular subtypes of GC, designated CS1, CS2, and CS3. These subtypes exhibited significant differences in survival outcomes, activation of cancer-related pathways, immune microenvironment composition, genomic alterations, and responses to immunotherapy and chemotherapy. Notably, Cathepsin V (CTSV) was significantly downregulated in the immunologically active and highly responsive CS3 subtype, while it was upregulated in the immunologically exhausted CS2 subtype. These findings suggest that CTSV could serve as both a prognostic marker and a molecular classifier. Furthermore, this study provides the first evidence of CTSV's high expression in GC and its potential role in tumor progression. The novel clustering approach, based on ten clustering algorithms and comprehensive analysis of multi-omics data in gastric cancer, can guide prognosis, characterize different clinical and biological features, and elucidate the tumor immune microenvironment, providing insights into the intratumor heterogeneity of GC and potential novel therapeutic strategies. Additionally, CTSV emerges as a prognostic marker linked to tumor immunity and disease progression, which lays the foundation for improved stratification strategies and the development of targeted therapeutic approaches in GC.

Bone metastatic lesions typically associate with suboptimal responses to immune checkpoint blockade (ICB) therapies. In this study, we observed that across multiple clinical cohorts and a variety of mouse models, the presence of osseous metastases induces ICB resistance in extraosseous tumors. Mechanistically, this long-distance communication is mediated by osseous tumor-conditioned osteoclasts producing osteopontin (OPN). Through circulation, OPN reprograms the extraosseous tumor microenvironment and impairs T cell recruitment and differentiation of CD8+TCF1+ precursor cells, an essential population for ICB efficacy. In mice, ICB responsiveness is restored by αRANKL blockade of osteoclastogenesis, neutralization of OPN in circulation, or tissue-specific depletion of OPN in osteoclasts. Both the mode of action and therapeutic benefit were validated in clinical cohorts with the αRANKL-ICB combinatory regimen. These findings establish bone as a specific immunoregulatory organ exploited by tumor metastasis and suggest osteoclastogenesis as a promising target to improve ICB prognosis in patients with bone metastasis.

Bladder cancer (BLCA) is a highly heterogeneous disease that presents challenges in predicting prognosis and treatment response. Cancer stem cells are key drivers of tumor development, progression, metastasis, and treatment resistance. The features and prognostic significance of stemness in bladder cancer need further investigation.

We used bladder cancer datasets from the TCGA and GEO databases, based on stemness gene sets from the StemChecker database, to identify stemness subtypes using the consensus clustering algorithm. We calculated the mRNA expression-based stemness index (mRNAsi) using the OCLR algorithm. We compared the differences in overall survival, genomic characteristics, tumor microenvironment, and treatment response between the stemness subtypes. We constructed the stemness subtype classifier using machine learning algorithms such as LASSO regression, random forest, and multivariate logistic regression. The function of the classifier gene was validated through experiments.

We divided bladder cancer patients into two subtypes based on the enrichment scores of stemness gene sets. Patients within subtype 1 have a higher mRNAsi score, a better survival rate, an antitumor microenvironment, and higher sensitivity to immunotherapy, while patients within subtype 2 show higher aneuploidy scores, greater homologous recombination defects, an elevated tumor mutation burden, and increased chemotherapy sensitivity. We constructed a stemness subtype classifier based on six differentially expressed genes between the two subtypes. The classifier demonstrated good performance in predicting prognosis on three additional datasets from the GEO database and two non-muscle invasive bladder cancer datasets. Through tumor sphere formation experiments and western blotting, we found that TNFAIP6, out of the six classifier genes, maintains stemness. TNFAIP6 silencing also facilitated the chemotherapy response of cisplatin, docetaxel, and paclitaxel on bladder cancer cells. Furthermore, decreasing TNFAIP6 expression caused the immune checkpoint gene PD-L1 to downregulate.

This study provided valuable insights into the heterogeneity of BLCA stemness, and the stemness subtype classifier may facilitate molecular classification and personalized treatment selection for BLCA patients. Besides, TNFAIP6 may serve as a potential future stemness target guiding bladder cancer chemotherapy and immunotherapy.

Cancer-associated fibroblasts (CAFs) are an important component of the stroma. Studies showed that CAFs were pivotally in glioma progression which have long been considered a promising therapeutic target. Therefore, the identification of prognostic CAF markers might facilitate the development of novel diagnostic and therapeutic approaches. A total of 1333 glioma samples were obtained from the TCGA and CGGA datasets. The EPIC, MCP-counter, and xCell algorithms were used to evaluate the relative proportion of CAFs in glioma. CAF markers were identified by the single-cell RNA-seq datasets (GSE141383) from the Tumor Immune Single-Cell Hub database. Unsupervised consensus clustering was used to divide the glioma patients into different distinct subgroups. The least absolute shrinkage and selection operator regression model was utilized to establish a CAF-related signature (CRS). Finally, the prognostic CAF markers were further validated in clinical specimens by RT‒qPCR. Combined single-cell RNA-seq analysis and differential expression analysis of samples with high and low proportions of CAFs revealed 23 prognostic CAF markers. By using unsupervised consensus clustering, glioma patients were divided into two distinct subtypes. Subsequently, based on 18 differentially expressed prognostic CAF markers between the two CAF subtypes, we developed and validated a new CRS model (including PCOLCE, TIMP1, and CLIC1). The nomogram and calibration curves indicated that the CRS was an accurate prognostic marker for glioma. In addition, patients in the high-CRS score group had higher immune infiltration and tumor mutation burden levels. Moreover, the CRS score had the potential to predict the response to immune checkpoint blockade (ICB) therapy and chemotherapy. Finally, the expression profiles of three CAF markers were verified by RT‒qPCR. In general, our study classified glioma patients into distinct subgroups based on CAF markers, which will facilitate the development of individualized therapy. We also provided insights into the role of the CRS in predicting the response to ICB and chemotherapy in glioma patients.

Mitophagy is an essential cellular process that is conserved and crucial for maintaining cellular balance by selectively eliminating malfunctioning mitochondria. However, there is still limited knowledge regarding the influence of mitophagy-related genes (MRGs) on the prognosis and response to treatment of triple-negative breast cancer (TNBC). In here, the TCGA and GEO databases were used to acquire the transcriptomic and clinical information of patients with TNBC, correspondingly. Using LASSO and multivariable Cox regression analyses, a risk signature related to mitophagy was established based on the prognostic MRGs. The prognostic signature associated with mitophagy consisted of five genes (BSG, JMJD6, DNAJA3, DISC1, and SQSTM1) and independently predicted the prognosis of patients with TNBC, regardless of clinical factors (p < 0.05). Patients classified within the high-risk group demonstrated significantly lower overall survival rates when contrasted with those in the low-risk group. The model exhibited excellent performance in predicting survival and risk stratification, as evidenced by the receiver operating characteristic and C-index. The findings stayed unchanged following external validation. Moreover, we observed a notable variation in the tumor immune microenvironment among the different risk categories. Patients with a low risk of TNBC demonstrated a more favorable response to immunotherapy compared to patients with a high risk. In conclusion, our study uncovered the possible impacts of MRGs on the tumor microenvironment, clinical and pathological characteristics, and outlook of TNBC. The CRG-related signature was strongly linked to the immune response against TNBC and has the potential to serve as a valuable tool in predicting the prognosis and immunotherapy response of patients.

In early gastric cancer, local recurrence develops after endoscopic resection by field cancerization. Understanding the nature of cancer-prone environments is important to establish effective strategies to prevent recurrence. We hypothesized that the molecular/immune profiles in non-tumor (cancer-prone) tissue differ according to the relative distance from the gastric tumor. For this purpose, we performed whole-exome and transcriptome sequencing of 16 early gastric cancer samples with paired non-tumor mucosa 1 cm (N1) and 3 cm (N3) away from the tumor. The whole exome sequencing revealed mutations in both the tumor and non-tumor mucosa. TTN was the most frequently altered gene in tumors (31 %) and was the second most frequently altered gene in N1 (25 %) samples; however, the mutation rate was significantly lower in N3 (12 %) samples (P = 0.0046). Moreover, the expression levels of TTN mRNA were higher in tumors than in the N1 and N3 samples and were significantly associated with TTN mutations (P = 0.04). TP53 mutations were mainly observed in tumors (50 %) and in 6.3 % of N1, with no mutation detected in N3 samples. Transcriptome sequencing revealed that the expression of the epithelial-mesenchymal transition signature, mesenchymal signature, and proliferation signature was increased in tumors, whereas programmed death-ligand 1 expression was decreased in the non-tumor mucosa. In the tumor, although the numbers of M0/M1 macrophages, neutrophils, and eosinophils increased, plasma cell numbers were markedly decreased compared to non-tumor mucosa. In conclusion, non-tumor mucosa at 1 cm and 3 cm from the tumor harbored different genomic, transcriptomic, and immune cell profiles. The non-tumor mucosa closer to the tumor (1 cm) exhibited similar genomic and transcriptomic features. These findings can offer clinical guidance for acquiring a safe horizontal margin in endoscopic resection for early gastric cancer.

Recent studies have highlighted the role of RNA modification, that is, the dysregulation of epitranscriptomics, in tumorigenesis and progression. The potential for undoing epigenetic changes may develop novel therapeutic and prognostic approaches. However, the roles of these RNA modifications in the tumor microenvironment (TME) are still unknown.

We assessed the expression properties and genetic alterations of 26 RNA modification writers, including adenosine-to-inosine RNA editing, alternative polyadenylation, m1A, and m6A in 502 lung adenocarcinoma (LUAD) samples from the Cancer Genome Atlas (TCGA) datasets. Then, we used differentially expressed gene (DEGs) to develop a signature for predicting patient outcomes, which was dubbed the "writer score" for RNA-modified writers. In addition, we analyzed the association between TME features, molecular subtypes, treatment sensitivity, and immunotherapy efficacy.

We comprehensively evaluated the changes in multilayer RNA modification writers and identified the role of RNA modification writer expression imbalances in LUAD emergence and progression. Additionally, we constructed a risk-score model based on six LUAD prognosis-associated differentially expressed RNA modification writer genes. Kaplan-Meier (K-M) analyses revealed that the low risk-score signature had high overall patient survival. The predictive significance of the risk-score model was demonstrated using both univariate and multivariate Cox analyses. The risk-score model was positively correlated with the immune- and proliferation-related pathways. In response to anti-cancer treatment, high-risk score is related with high TMB, which has been discovered to correlate with immunotherapy effectiveness.

This study showed a strong correlation between the TME variety, level of complexity, and the four types of RNA modification writers. In addition, this scoring system could potentially predict effective immunotherapy and deepens our understanding of TME characteristics.

The purpose of this review is to explore the molecular foundations of ocular surface squamous neoplasia (OSSN), focusing on the genetic and epigenetic aspects. While current management strategies include surgical excision and medical therapies, the understanding of OSSN's molecular basis remains limited, hindering the development of targeted treatments.

A comprehensive MEDLINE search was conducted for literature published between January 1993 and October 2023. Only studies with original data on molecular, genetic, or epigenetic mechanisms, such as mutations, gene expression, and genetic predispositions were included. Articles were excluded if they focused solely on clinical management without addressing these factors, or if they were reviews, editorials, or opinion pieces.

The search yielded a total of 108 articles, out of which 39 articles met the criteria for further analysis. Investigations into OSSN have identified key DNA mutations in the TP53, HGF, EGFR, TERT, and CDKN2A genes, indicating common oncogenic pathways shared with other squamous cell carcinomas (SCCs). Significant epigenetic changes were identified, including DNA methylation, histone modifications, and altered miRNA expression patterns. Epigenetic dysregulation of critical tumor suppressors and oncoproteins, further highlight the complex genetic landscape of OSSN.

The molecular alterations identified in OSSN not only enhance our understanding of its biology but also have potential as novel biomarkers for early detection, prognostic evaluation, and as therapeutic targets. The identification of genetic and epigenetic markers in OSSN signifies progress towards personalized medicine approaches. Further studies and collaborative efforts are essential to validate these molecular markers and translate them into clinical practice, potentially revolutionizing OSSN management and improving patient outcomes.

This study investigates the impact of Titin (TTN) gene mutations on radiotherapy sensitivity in rectum adenocarcinoma (READ) by examining changes in the tumour immune microenvironment.

Data on gene expression and mutations in READ were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Bioinformatics analysis explored the correlation between TTN mutations and immune cell infiltration. In vitro, lentiviral vectors were used to assess TTN mutations' effects on ANKRD1 expression in two READ cell lines. ANKRD1 was overexpressed, and clonogenic assays evaluated radiotherapy sensitivity. Flow cytometry, immunofluorescence, and comet assays examined mutations' impact on cell cycle, apoptosis, and DNA damage response (DDR). An in vivo mouse model and formalin-fixed paraffin-embedded samples from locally advanced rectal cancer (LARC) patients before and after radiotherapy were analyzed, followed by prognostic evaluation.

Bioinformatics revealed that TTN mutations increase radiation sensitivity in LARC by slowing cell proliferation, promoting apoptosis, and reducing DDR. TTN mutations also inhibit ANKRD1 expression via JUN disruption and enhance CD4/CD8 T-cell infiltration, improving anti-tumour immunity and outcomes. Observations from the clinical study showed a substantial decline in ANKRD1 expression levels alongside a notable surge in the counts of CD4+ and CD8+ T cells after undergoing radiotherapy. Patients with TTN mutations, low ANKRD1 expression, and high densities of CD4+ and CD8+ T cells had longer 3-year disease-free survival in READ.

Our findings reveal that TTN mutations can serve as biomarkers for enhanced radiotherapy sensitivity in READ. By altering the tumour's immune microenvironment, these mutations may provide a novel target for personalized radiotherapy strategies, potentially improving therapeutic outcomes in patients with READ.

The association between TTN mutations and tumour mutation burden, as well as immune cell infiltration in READ, is examined. TTN mutations enhance the radiation sensitivity of READ cells and weaken DNA damage repair in response to radiation. TTN mutations increase the radiation sensitivity of READ cells by inhibiting ANKRD1. The infiltration of CD8+ and CD4+ T cells induced by TTN mutations is essential for anti-tumour immunity. TTN mutations serve as a biomarker for the pathological response to preoperative radiotherapy in READ.

Cellular senescence is a key promoter of tumorigenesis and malignant progression. This study aimed to develop a predictive model for assessing cellular senescence in gastric cancer (GC) outcomes. We identified senescence-related genes and lncRNAs from 375 stomach adenocarcinoma (STAD) patients and established a prognostic senescence score using multivariate Cox regression, validated in testing, TCGA-STAD and the combined TCGA-COAD and READ cohorts. The model's predictive efficacy was evaluated across clinical subgroups, tumour microenvironments and immune cell infiltration. A total of 116 senescence-related lncRNAs were filtered, and patients were clustered into two senescent subtypes. The lncRNA signature identified LINC01579 as an independent prognostic factor for GC. Low-risk groups showed prolonged overall survival, increased immune cell infiltration and reduced mutation load. Downregulation of LINC01579 using antisense oligonucleotides (ASOs) on normal human fibroblasts decreased cellular proliferation and migration in GC. Collectively, this study established and validated a promising prognostic model connecting senescence-related lncRNAs and clinical outcome in GC and provided potential senescence-related biomarkers for GC prognosis prediction.

Gastric cancer is one of the major cancers with high cancer mortality and shows significant heterogeneity. The development of precise prognostic models is crucial for advancing treatment strategies. Recognizing the pivotal role of DNA damage in tumor progression, we conducted a consensus clustering analysis of DNA damage-related genes to categorize gastric cancer patients from the TCGA clinical cohort into distinct subtypes. Prognostic models were then constructed utilizing machine learning algorithms following Cox regression with differentially expressed genes. Validation was performed using the GSE gastric cancer cohort. Additionally, we investigated other characteristic responses of patients through gene mapping and drug sensitivity analysis. This study 12 differentially prognostic signature genes between the 2 DNA damage subtypes identified were used to calculate risk scores for the patients. This score predicts the prognosis of patients with gastric cancer and their overall survival time. Higher risk scores mean less drug sensitivity, lower survival, and possibly a poorer response to immunotherapy. Our findings provide the basis for future studies targeting DNA damage and its immune microenvironment to improve prognosis and response to immunotherapy.

Immune checkpoint inhibitors (ICIs), particularly anti-programmed death 1 (PD-1)/ programmed death ligand 1 (PD-L1) antibodies, have led to significant progress in lung cancer treatment. However, only a minority of patients have responses to these therapies. Detecting peripheral blood of circulating tumor DNA (ctDNA) allows minimally invasive diagnosis, characterization, and monitoring of lung cancer. ctDNA has potential to be a prognostic biomarker and a predictor of the response to ICI therapy since it can indicate the genomic status and tumor burden. Recent studies on lung cancer have shown that pretreatment ctDNA analysis can detect residual proliferative disease in the adjuvant immunotherapy setting and evaluate tumor burden in patients with metastatic disease. Early ctDNA dynamics can not only predict the clinical outcome of ICI therapy but also help distinguish between pseudoprogression and real progression. Furthermore, in addition to quantitative assessment, ctDNA can also detect genetic predictors of response to ICI therapy. However, barriers still exist in the application of ctDNA analysis in clinical lung cancer treatment. The predictive value of ctDNA in lung cancer immunotherapy requires further identification and resolution of these challenges. This review aims to summarize the existing data of ctDNA analysis in patients receiving immunotherapy for lung cancer, understand the limitations of clinical treatment, and discuss future research directions.

Altered expression and activity of solute carrier family 4 member 4 (SLC4A4) could affect the growth, survival and metastasis of tumor cells. Currently, the role of SLC4A4 in lung adenocarcinoma (LUAD) immunotherapy and prognosis was not entirely clear.

We analyzed SLC4A4 expression in LUAD tissues and cell lines using quantitative reverse transcription-polymerase chain reaction, Western blotting, and immunohistochemistry. The effects of SLC4A4 overexpression on angiogenesis, cell migration, invasion, and epithelial-mesenchymal transition were examined. Public databases helped construct a risk model evaluating SLC4A4's expression on LUAD prognosis and immunotherapy response. Additionally, a xenograft model, flow cytometry, and enzyme-linked immunosorbent assay further explored SLC4A4's role in tumor immune microenvironment infiltration.

Upregulation of SLC4A4 promoted apoptosis in the LUAD cell line and significantly inhibited the migration and invasive ability of cancer cells (P<0.01). A total of 10 key genes (including SIGLEC6, RHOV, PIR, MOB3B, MIR3135B, LPAR6, KRT8, ITGA2, CPS1, and C6) were screened according to SLC4A4 expression, immune score and stromal score, and a prognostic model with good outcome was constructed (AUC values of which in the training cohort at 1,3, and 5 years reached 0.73, 0.73, and 0.72, respectively). Importantly, we demonstrated that high expression of SLC4A4 was able to increase the proliferation level and cytokine secretion of CD8+ T cells for the purpose of promoting the immune system response to LUAD.

Our study revealed that SLC4A4 can serve as a prognostic indicator for LUAD, providing new insights into the treatment and diagnosis of LUAD.

Gastric cancer (GC) is considered a global health crisis due to the scarcity of early diagnostic methods. Numerous studies have substantiated the involvement of histone acetylation imbalance in the progression of diverse tumor types. The potential roles of long non-coding RNA (lncRNA) in improving prognostic, predictive as well as therapeutic approaches in cancers have made it a major hotspot in recent years. Nevertheless, existent studies have never concerned the prognostic and clinical value of histone acetylation-related lncRNAs (HARlncs) in GC. Based on the aforementioned rationale, we developed a prognostic model incorporating four HARlncs-AC114730.1, AL445250.1, LINC01778, and AL163953.1-which demonstrated potential as an independent predictor of prognosis. Subsequently, GC patients were stratified into high-risk and low-risk groups. The low-risk group exhibited significantly higher overall survival (OS) compared to the high-risk group. Based on the analyses of the tumor microenvironment (TME) and immune responses, significant differences were observed between the two risk groups in terms of immune cell infiltration, immune checkpoint (ICP) expression, and other TME alterations. Furthermore, the sensitivity of GC patients to some chemotherapeutic drugs and the discrepant biological behaviors of three tumor clusters were studied in this model. In summary, we developed an effective HARlncs model with the objective of offering novel prognostic prediction methods and identifying potential therapeutic targets for GC patients.

The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD.

In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD.

We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma.

Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Genome instability (GI) is a hallmark of esophageal squamous cell carcinoma (ESCC) while factors affecting GI remain unclear.

Here, we aimed to characterize genomic events representing specific mechanisms of GI based on 201 ESCC samples and validated our findings at the patient, single-cell and cancer cell-line levels, including a newly generated multi-omics dataset of the trial NCT04006041.

A two-gene (AHNAK and AHNAK2) mutation signature was identified to define the "AHNAK1/2-mutant" cancer subtype. Single-cell-assisted multi-omics analysis showed that this subtype had a higher neoantigen load, active antigen presentation, and proficient CD8 + T cell infiltrations, which were validated at pan-cancer levels. Mechanistically, AHNAK1/2-mutant ESCC was characterized by impaired response of TGF-β and the inefficient alternative end-join repair (Alt-EJ) that might promote GI. Knockdown of AHNAK in ESCC cell lines resulted in more Alt-EJ events and increased sensitivities to cisplatin. Furthermore, this two-gene signature accurately predicted better responses to DNA-damaging therapy in various clinical settings (HR ≈ 0.25). The two-gene signature predicted higher pCR rates in ESCCs receiving neoadjuvant immunotherapy-involved treatment. Finally, a molecular classification scheme was built and outperformed established molecular typing models in the prognosis stratification of ESCC patients.

Our study extended our understanding of the AHNAK family in promoting GI and selecting treatment responders of ESCC.

Most primary cutaneous melanomas have pathogenesis driven by ultraviolet exposure and genetic mutations, whereas acral lentiginous melanoma (ALM) and metastatic melanoma are much less, if at all, linked with the former. Thus, we evaluated both ultraviolet related and non-ultraviolet related melanomas. Mutations in the MUC16 and TTN genes commonly occur concurrently in these melanoma patients, but their combined prognostic significance stratified by gender and cancer subtype remains unclear.

The cBioPortal database was queried for melanoma studies and returned 16 independent studies. Data from 2447 melanoma patients were utilized including those with ALM, cutaneous melanoma (CM), and melanoma of unknown primary (MUP). Patients were grouped based on the presence or absence of MUC16 and TTN mutations. Univariate Cox regression and Student's t-tests were used to analyze hazard ratios and total mutation count comparisons, respectively.

TTN mutations, either alone or concurrently with MUC16 mutations, significantly correlated with worse prognosis overall, in both genders, and in CM patients. ALM patients with both mutations had better prognoses than CM patients, while ALM patients with neither mutation had worse prognosis than CM patients. For MUP patients, only MUC16 mutations correlated with worse prognosis. ALM patients with neither MUC16 nor TTN mutations had significantly more total mutations than MUP patients, followed by CM patients.

TTN mutations are a potential marker of poor prognosis in melanoma, which is amplified in the presence of concurrent MUC16 mutations. ALM patients with neither gene mutations had worse prognosis, suggesting a protective effect of having both MUC16 and TTN mutations. Only MUC16 mutations conferred a worse prognosis for MUP patients. Comprehensive genetic profiling in melanoma patients may facilitate personalized treatment strategies to optimize patient outcomes.

High-grade appendiceal adenocarcinomas (HGAA) with peritoneal metastases (PMs) are associated with poor survival. Hyperthermic intraperitoneal chemotherapy (HIPEC) is a novel treatment approach for unresectable HGAA-PM. However, its influence on immunogenomic profiles has not yet been fully explored.

We obtained 79 samples of metastatic peritoneal tumor deposits from patients diagnosed with HGAA and performed whole-exome sequencing, RNA sequencing, and immunoprofiling before and after HIPEC. Tumor biopsies were subjected to immunogenomic profiling to detect mutational signatures and immune populations associated with oncologic outcomes.

Fifteen patients with HGAA-PMs were included in the study. The median progression-free survival (PFS) was 6.7 months (2.7-25.3) and the median overall survival was 11.4 months (4.7-42). Mucin-associated genes (MUC16, MUC3A, and MUC5AC) and titin (TTN) had the highest mutation frequencies. Mutational signatures such as single-base substitution 29 and doublet-base substitution 11 were present in >50% of single-base and double-base mutations. Higher PD-L1 coexpression on CD8+ T cells demonstrated a higher PFS both intratumorally (P = .019) and at the margin (P = .025).

HIPEC-associated mutational signatures were identified in HGAA-PMs. Elevated PD-L1+ cytotoxic T-cell populations after HIPEC had better PFS, offering valuable insights for prognostication in the context of HIPEC treatment.

Glucose-6-Phosphate Dehydrogenase (G6PD) is a crucial enzyme that executes the pentose phosphate pathway. Due to its critical nodal position in the metabolic network, it is associated with different forms of cancer tumorigeneses and progression. Nonetheless, its functional role and molecular mechanism in lung cancer remain unknown. The present study provides intricate information associated with G6PD and Lung Cancer. Varieties of public datasets were retrieved by us, including UALCAN, TCGA, cBioPortal, and the UCSC Xena browser. The data obtained were used to assess the expression of G6PD, its clinical features, epigenetic regulation, relationship with tumour infiltration, tumour mutation burden, microsatellite instability, tumour microenvironment, immune checkpoint genes, genomic alteration, and patient's overall survival rate. The present study revealed that the G6PD expression was correlated with the clinical features of lung cancer including disease stage, race, sex, age, smoking habits, and lymph node metastasis. Moreover, the expression profile of G6PD also imparts epigenetic changes by modulating the DNA promoter methylation activity. Methylation of promoters changes the expression of various transcription factors, genes leading to an influence on the immune system. These events linked with G6PD-related mutational gene alterations (FAM3A, LAG3, p53, KRAS). The entire circumstance influences the patient's overall survival rate and poor prognosis. Functional investigation using STRING, GO, and KEGG found that G6PD primarily engages in hallmark functions (metabolism, immunological responses, proliferation, apoptosis, p53, HIF-1, FOXO, PI3K-AKT signaling). This work provides a wide knowledge of G6PD's function in lung cancer, as well as a theoretical foundation for possible prognostic therapeutic markers.

Transcriptional heterogeneity of tumor-associated macrophages (TAMs) has been investigated in individual cancers, but the extent to which these states transcend tumor types and represent a general feature of cancer remains unclear. We performed pan-cancer single-cell RNA sequencing analysis across nine cancer types and identified distinct monocyte/TAM composition patterns. Using spatial analysis from clinical study tissues, we assessed TAM functions in shaping the tumor microenvironment (TME) and influencing immunotherapy. Two specific TAM clusters (pro-inflammatory and pro-tumor) and four TME subtypes showed distinct immunological features, genomic profiles, immunotherapy responses, and cancer prognosis. Pro-inflammatory TAMs resided in immune-enriched niches with exhausted CD8+ T cells, while pro-tumor TAMs were restricted to niches associated with a T-cell-excluded phenotype and hypoxia. We developed a machine learning model to predict immune checkpoint blockade response by integrating TAMs and clinical data. Our study comprehensively characterizes the common features of TAMs and highlights their interaction with the TME.

Immunotherapy has revolutionized the treatment of various types of tumors, but there has been no breakthrough in the treatment of gliomas. The aim of this study is to discover valuable immunotherapy target in glioma, analyze its expression in glioma and the related microenvironment, explore potential immunotherapy strategies, and propose new possibilities for the treatment of gliomas.

Immunohistochemistry (IHC) and multiplex fluorescence immunohistochemistry (mIHC) were used to analyze the expression of common immune markers and checkpoints in 187 glioma patients from Sun Yat-sen University Caner Center (SYSUCC). Bioinformatics analysis was used to examine the expression of TIM-3 in different macrophages using the Chinese Glioma Genome Atlas (CGGA) single-cell sequencing database. The Kaplan-Meier curve was used to predict the prognostic value of samples with high TIM-3 and CD68 expression. The R package was used to analyze the somatic mutation status and the sensitivity of small molecule inhibitors in TIM-3/CD68 double-high expression samples.

TIM-3 is a relatively highly expressed immune checkpoint in glioma. Unlike other tumors, TIM-3 is mainly expressed on macrophages in the glioma microenvironment. TIM-3/CD68 double-high expression suggests poor survival in glioma and may be a new upgrade marker in both IDH-mutant glioma and IDH-wildtype low-grade glioma (LGG) glioma (P < 0.01). Exploring the combination of TIM-3 inhibitors and p38 MAPK inhibitor may be a potential treatment direction for TIM-3/CD68 double high expression gliomas in the future.

The combination of TIM-3 and CD68 holds significant importance as a potential target for both prognosis and therapeutic intervention in glioma.

Acute Myeloid Leukemia (AML) exhibits a wide array of phenotypic manifestations, progression patterns, and heterogeneous responses to immunotherapies, suggesting involvement of complex immunobiological mechanisms. This investigation aimed to develop an integrated prognostic model for AML by incorporating cancer driver genes, along with clinical and phenotypic characteristics of the disease, and to assess its implications for immunotherapy responsiveness.

Critical oncogenic driver genes linked to survival were identified by screening primary effector and corresponding gene pairs using data from The Cancer Genome Atlas (TCGA), through univariate Cox proportional hazard regression analysis. This was independently verified using dataset GSE37642. Primary effector genes were further refined using LASSO regression. Transcriptomic profiling was quantified using multivariate Cox regression, and the derived prognostic score was subsequently validated. Finally, a multivariate Cox regression model was developed, incorporating the transcriptomic score along with clinical parameters such as age, gender, and French-American-British (FAB) classification subtype. The 'Accurate Prediction Model of AML Overall Survival Score' (APMAO) was developed and subsequently validated. Investigations were conducted into functional pathway enrichment, alterations in the gene mutational landscape, and the extent of immune cell infiltration associated with varying APMAO scores. To further investigate the potential of APMAO scores as a predictive biomarker for responsiveness to cancer immunotherapy, we conducted a series of analyses. These included examining the expression profiles of genes related to immune checkpoints, the interferon-gamma signaling pathway, and m6A regulation. Additionally, we explored the relationship between these gene expression patterns and the Tumor Immune Dysfunction and Exclusion (TIDE) dysfunction scores.

Through the screening of 95 cancer genes associated with survival and 313 interacting gene pairs, seven genes (ACSL6, MAP3K1, CHIC2, HIP1, PTPN6, TFEB, and DAXX) were identified, leading to the derivation of a transcriptional score. Age and the transcriptional score were significant predictors in Cox regression analysis and were integral to the development of the final APMAO model, which exhibited an AUC greater than 0.75 and was successfully validated. Notable differences were observed in the distribution of the transcriptional score, age, cytogenetic risk categories, and French-American-British (FAB) classification between high and low APMAO groups. Samples with high APMAO scores demonstrated significantly higher mutation rates and pathway enrichments in NFKB, TNF, JAK-STAT, and NOTCH signaling. Additionally, variations in immune cell infiltration and immune checkpoint expression, activation of the interferon-γ pathway, and expression of m6A regulators were noted, including a negative correlation between CD160, m6A expression, and APMAO scores.

The combined APMAO score integrating transcriptional and clinical parameters demonstrated robust prognostic performance in predicting AML survival outcomes. It was linked to unique phenotypic characteristics, distinctive immune and mutational profiles, and patterns of expression for markers related to immunotherapy sensitivity. These observations suggest the potential for facilitating precision immunotherapy and advocate for its exploration in upcoming clinical trials.

Previous studies suggested that zinc finger protein 536 (ZNF536) was abundant in the central brain and regulated neuronal differentiation. However, the role of ZNF536 in cancer has remained unclear.

ZNF536 mutation, copy number alteration, DNA methylation, and RNA expression were explored using public portals. Data from The Cancer Genome Atlas (TCGA) were utilized to analyze pathways and tumor microenvironment (TME), with a focus on prognosis in both TCGA and immunotherapy pan-cancer cohorts. Methylated ZNF536 from small cell lung cancer (SCLC) cell lines were utilized to train with probes for conducting enrichment analysis. Single-cell RNA profile demonstrated the sublocalization and co-expression of ZNF536, and validated its targets by qPCR.

Genetic alterations in ZNF536 were found to be high-frequency and a single sample could harbor different variations. ZNF536 at chromosome 19q12 exerted a bypass effect on CCNE1, supported by CRISPR data. For lung cancer, ZNF536 mutation was associated with longer survival in primary lung adenocarcinoma (LUAD), but its prognosis was poor in metastatic LUAD and SCLC. Importantly, ZNF536 mutation and amplification had opposite prognoses in Stand Up To Cancer-Mark Foundation (SU2C-MARK) LUAD cohort. ZNF536 mutation altered the patterns of genomic alterations in tumors, and had distinct impacts on the signaling pathways and TME compared to ZNF536 amplification. Additionally, ZNF536 expression was predominantly in endocrine tumors and brain tissues. High-dimensional analysis supported this finding and further revealed regulators of ZNF536. Considering that the methylation of ZNF536 was involved in the synaptic pathway associated with neuroendocrine neoplasms, demonstrating both diagnostic and prognostic value. Moreover, we experimentally verified ZNF536 upregulated neuroendocrine markers.

Our results showed that ZNF536 alterations in cancer, including variations in copy number, mutation, and methylation. We proved the involvement of ZNF536 in neuroendocrine regulation, and identified highly altered ZNF536 as a potential biomarker for immunotherapy.

Gastric carcinoma (GC) is a high heterogeneity and malignant tumor with a poor prognosis. The current implementation of immunotherapy in GC is limited due to the insufficient exploration of immune-related mutations and speculated early mutation events. Therefore, we performed whole-exome sequencing on 40 patients with GC to explore their genetic characteristics, shedding light on the order of genetic events, somatic mutations impacting the immune microenvironment, and potential biomarkers for immunotherapy. Regarding genetic events, TP53 disruptions were identified as frequent and early events in GC progression, often occurring alongside other gene mutations. The mutations occurring in GANS, SMAD4, and POLE were early independent events. Patients harboring CSMD3, FAT4, FLG, KMT2C, LRP1B, MUC5B, MUC16, PLEC, RNF43, SYNE1, TP53, TTN, XIRP2, and ZFHX4 mutations tended to have decreased B cells, T cells, macrophage, neutrophil, and dendritic cells infiltration, except for the ARID1A gene mutations. We also found patients with microsatellite instability-high tumors had higher homologous recombination deficiency (HRD) scores. HRD showed a positive correlation with tumor mutational burden, which might serve as indirect evidence supporting the potential of HRD as a biomarker for GC. These findings highlighted GC's high heterogeneity and complexity and provided valuable insights into the somatic mutations that affect early genetic progression and immune microenvironment.

In the precision medicine era, identifying predictive factors to select patients most likely to benefit from treatment with immunological agents is a crucial and open challenge in oncology.

This paper presents a pan-cancer analysis of Tumor Mutational Burden (TMB). We developed a novel computational pipeline, TMBcalc, to calculate the TMB. Our methodology can identify small and reliable gene signatures to estimate TMB from custom targeted-sequencing panels. For this purpose, our pipeline has been trained on top of 17 cancer types data obtained from TCGA.

Our results show that TMB, computed through the identified signature, strongly correlates with TMB obtained from whole-exome sequencing (WES).

We have rigorously analyzed the effectiveness of our methodology on top of several independent datasets. In particular we conducted a comprehensive testing on: (i) 126 samples sourced from the TCGA database; few independent whole-exome sequencing (WES) datasets linked to colon, breast, and liver cancers, all acquired from the EGA and the ICGC Data Portal. This rigorous evaluation clearly highlights the robustness and practicality of our approach, positioning it as a promising avenue for driving substantial progress within the realm of clinical practice.

Tryptophan (Trp) is an essential amino acid. Increasing evidence suggests that tryptophan metabolism plays a complex role in immune escape from Lung adenocarcinoma (LUAD). However, the role of long non-coding RNAs (lncRNAs) in tryptophan metabolism remains to be investigated.

This study uses The Cancer Genome Atlas (TCGA)-LUAD dataset as the training cohort, and several datasets from the Gene Expression Omnibus (GEO) database are merged into the validation cohort. Genes related to tryptophan metabolism were identified from the Molecular Signatures Database (MSigDB) database and further screened for lncRNAs with Trp-related expression. Subsequently, a prognostic signature of lncRNAs related to tryptophan metabolism was constructed using Cox regression analysis, (Least absolute shrinkage and selection operator regression) and LASSO analysis. The predictive performance of this risk score was validated by Kaplan-Meier (KM) survival analysis, (receiver operating characteristic) ROC curves, and nomograms. We also explored the differences in immune cell infiltration, immune cell function, tumor mutational load (TMB), tumor immune dysfunction and exclusion (TIDE), and anticancer drug sensitivity between high- and low-risk groups. Finally, we used real-time fluorescence quantitative PCR, CCK-8, colony formation, wound healing, transwell, flow cytometry, and nude mouse xenotransplantation models to elucidate the role of ZNF8-ERVK3-1 in LUAD.

We constructed 16 tryptophan metabolism-associated lncRNA prognostic models in LUAD patients. The risk score could be used as an independent prognostic indicator for the prognosis of LUAD patients. Kaplan-Meier survival analysis, ROC curves, and risk maps validated the prognostic value of the risk score. The high-risk and low-risk groups showed significant differences in phenotypes, such as the percentage of immune cell infiltration, immune cell function, gene mutation frequency, and anticancer drug sensitivity. In addition, patients with high-risk scores had higher TMB and TIDE scores compared to patients with low-risk scores. Finally, we found that ZNF8-ERVK3-1 was highly expressed in LUAD tissues and cell lines. A series of in vitro experiments showed that knockdown of ZNF8-ERVK3-1 inhibited cell proliferation, migration, and invasion, leading to cell cycle arrest in the G0/G1 phase and increased apoptosis. In vivo experiments with xenografts have shown that knocking down ZNF8-ERVK3-1 can significantly inhibit tumor size and tumor proliferation.

We constructed a new prognostic model for tryptophan metabolism-related lncRNA. The risk score was closely associated with common clinical features such as immune cell infiltration, immune-related function, TMB, and anticancer drug sensitivity. Knockdown of ZNF8-ERVK3-1 inhibited LUAD cell proliferation, migration, invasion, and G0/G1 phase blockade and promoted apoptosis.

Endometrioid endometrial carcinoma (EEC) stands as a prevalent gynecologic malignancy in developed regions. However, predicting relapse cases remains challenging, necessitating the identification of a novel biomarker for EEC relapse. The assessment of tumor mutational burden (TMB) is pivotal for immunotherapy in EEC patients. However, both whole-exome sequencing (WES) and targeted sequencing encountered application-related difficulties. In light of this, standardized and simplified techniques for TMB measurement are imperative. In this study, we employed WES on 25 EEC patients (12 relapsed cases and 13 non-relapsed cases) who accepted hysterectomy surgery (CHCAMS cohort). We additionally obtained a total of 391 tumor samples with clinicopathological features from TCGA website to broaden the study cohort. In the CHCAMS cohort, the TTN mutant group showed shorter progression-free survival (p < 0.001) and overall survival (p < 0.001) than TTN wild-type group. Additionally, we discovered that the number of TTN mutations per sample was significantly linked with TMB-WES in CHCAMS cohort and TCGA cohort (p < 0.05). And the number of TTN mutations per sample in POLE mutant group was greater than in the POLE wild-type group (p < 0.0001). In conclusion, TTN mutation may serve as a biomarker for EEC prognosis. TTN mutation is also associated with WES-TMB, and could be a simplified TMB measurement technique.

Peroxisome proliferator-activated receptors (PPARs) exert multiple functions in the initiation and progression of stomach adenocarcinomas (STAD). This study analyzed the relationship between PPARs and the immune status, molecular mutations, and drug therapy in STAD.

The expression profiles of three PPAR genes (PPARA, PPARD and PPARG) were downloaded from The Cancer Genome Atlas (TCGA) dataset to analyze their expression patterns across pan-cancer. The associations between PPARs and clinicopathologic features, prognosis, tumor microenvironment, genome mutation and drug sensitivity were also explored. Co-expression between two PPAR genes was calculated using Pearson analysis. Regulatory pathways of PPARs were scored using gene set variation analysis (GSVA) package. Quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, Cell Counting Kit-8 (CCK-8) assay and transwell assay were conducted to analyze the expression and function of the PPAR genes in STAD cell lines (AGS and SGC7901 cells).

PPARA, PPARD and PPARG were more abnormally expressed in STAD samples and cell lines when compared to most of 32 type cancers in TCGA. In STAD, the expression of PPARD was higher in Grade 3+4 and male patients, while that of PPARG was higher in patient with Grade 3+4 and age > 60. Patients in high-PPARA expression group tended to have longer survival time. Co-expression analysis revealed 6 genes significantly correlated with the three PPAR genes in STAD. Single-sample GSEA (ssGSEA) showed that the three PPAR genes were enriched in 23 pathways, including MITOTIC_SPINDLE, MYC_TARGETS_V1, E2F_TARGETS and were closely correlated with immune cells, including NK_cells_resting, T_cells_CD4_memory_resting, and macrophages_M0. Immune checkpoint genes (CD274, SIGLEC15) were abnormally expressed between high-PPAR expression and low-PPAR expression groups. TTN, MUC16, FAT2 and ANK3 genes had a high mutation frequency in both high-PPARA/PPARG and low-PPARA/PPARG expression group. Fourteen and two PPARA/PPARD drugs were identified to be able to effectively treat patients in high-PPARA/PPARG and low-PPARA/PPARG expression groups, respectively. We also found that the chemotherapy drug Vinorelbine was positively correlated with the three PPAR genes, showing the potential of Vinorelbine to serve as a treatment drug for STAD. Furthermore, cell experiments demonstrated that PPARG had higher expression in AGS and SGC7901 cells, and that inhibiting PPARG suppressed the viability, migration and invasion of AGS and SGC7901 cells.

The current results confirmed that the three PPAR genes (PPARA, PPARD and PPARG) affected STAD development through mediating immune microenvironment and genome mutation.

Oxidative stress promotes the development of stomach adenocarcinoma (STAD) and resistance of STAD patients to chemotherapy. This study developed a risk classification and prognostic model for STAD based on genes related to oxidative stress.

Univariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression analysis were performed using transcriptome data of STAD from The Cancer Genome Atlas (TCGA) and reactive oxygen species (ROS)-related genes from Gene Set Enrichment Analysis (GSEA) website to develop a risk model. Genetic landscape, pathway characteristics and immune characteristics between the two risk groups were assessed to evaluate patients' response to anti-tumor therapy. Further, a nomogram was created to evaluate the clinical outcomes of STAD patients. The mRNA levels of genes were detected by reverse transcription quantitative PCR (RT-qPCR).

Two ROS-related molecular subtypes (subtype C1 and C2) were classified, with subtype C2 having unfavorable prognosis, higher immune score, and greater infiltration of macrophages, myeloid-derived suppressor cells (MDSCs), mast cells, regulatory T cells, and C-C chemokine receptor (CCR). Five ROS-related genes (ASCL2, COMP, NOX1, PEG10, and VPREB3) were screened to develop a prognostic model, the robustness of which was validated in TCGA and external cohorts. RT-qPCR analysis showed that ASCL2, COMP, NOX1, and PEG10 were upregulated, while the mRNA level of VPREB3 was downregulated in gastric cancer cells. The risk score showed a negative relation to tumor mutation burden (TMB). Low-risk patients exhibited higher mutation frequencies of TTN, SYNE1, and ARID1A, higher response rate to immunotherapy and were more sensitive to 32 traditional chemotherapeutic drugs, while high-risk patients were sensitive to 13 drugs. Calibration curve and DCA confirmed the accuracy and reliability of the nomogram.

These findings provided novel understanding on the mechanism of ROS in STAD. The current study developed a ROS-related signature to help predict the prognosis of patients suffering from STAD and to guide personalized treatment.

About 7% of all cancer deaths are caused by pancreatic cancer (PCa). PCa is known for its lowest survival rates among all oncological diseases and heterogenic molecular profile. Enormous amount of genetic changes, including somatic mutations, exceeds the limits of routine clinical genetic laboratory tests and further stagnates the development of personalized treatments. We aimed to build a mutational landscape of PCa in the Russian population based on full exome next-generation sequencing (NGS) of the limited group of patients. Applying a machine learning model on full exome individual data we received personalized recommendations for targeted treatment options for each clinical case and summarized them in the unique therapeutic landscape.

Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification task. By combining natural language processing (NLP) and graph neural network techniques, TG468 effectively handles sparse and high-dimensional exome sequencing data. As a result, TG468 can distinguish survival time for patients who received ICI therapy and outperforms single gene biomarkers, TMB and some classical machine learning models. Additionally, TG468's prediction results facilitate the identification of immune status differences among specific patient types in the Cancer Genome Atlas dataset, providing a rationale for the model's predictions. Our approach represents a pioneering use of a GCN model to analyze exome data in patients undergoing ICI therapy and offers inspiration for future research using NLP technology to analyze exome sequencing data.

The goal of this paper was the evaluation of the changes in the expression profile of irisin, ghrelin, and titin in the carcinoma tissue and in the blood of patients with head and neck squamous cell carcinoma (HNSCC), including determining the profile of their expression in relation to patient nutrition. The study included 56 patients with diagnosed squamous cell carcinoma of HNSCC in the T3 and T4 stages of the disease. Healthy control tissue specimens were collected from an area 10 mm outside the histologically negative margin. In turn, the blood and serum from the control group came from healthy volunteers treated for non-oncologic reasons (n = 70). The molecular analysis allowed us to determine the profile of irisin, ghrelin, and titin methylation, evaluate their expression on the level of mRNA (quantitative Reverse Transcription Polymerase Chain Reaction; qRT-PCR) and protein (Enzyme-Linked Immunosorbent Assay Reaction; ELISA) in the carcinoma tissue and the margin of healthy tissue, as well as in serum of patients in the study and control groups. At the start of our observations, a Body Mass Index (BMI) < 18.5 was noted in 42 of the patients, while six months after the treatment a BMI < 18.5 was noted in 29 patients. We also noted a decrease in the expression of irisin, ghrelin, and titin both on the level of mRNA and protein, as well as a potential regulation of their expression via DNA methylation. There is no convincing evidence that the proteins assayed in the present work are specific with regard to HNSSC.

Despite the remarkable efficacy of PD-1-associated immune checkpoint inhibitors in treating cutaneous melanoma (CM), the inconsistency in the expression of PD-1 and its ligand PD-L1, and resulting variability in the effectiveness of immunotherapy, present significant challenges for clinical application. Therefore, further research is necessary to identify tumor-related biomarkers that can predict the prognosis of immunotherapy. Tertiary lymphoid structures (TLSs) have been recognized as a crucial factor in predicting the response of immune checkpoint inhibitors in solid tumors, including CM. However, the study of TLSs in CM is not yet comprehensive. Gene expression profiles have been shown to correlate with CM risk stratification and patient outcomes. In this study, we identified TLS-related genes that can be used for prognostic purposes and developed a corresponding risk model. The impact of TLS-related genes on clinicopathological characteristics, immune infiltration and drug susceptibility was also explored. Our biological function enrichment analysis provided preliminary evidence of related signaling pathways. Our findings provide a new perspective on risk stratification and individualized precision therapy for CM.