SEARCH · Search PubMed

Results for “TCGA”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12Linked to original sources

PubMed2025

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis of methylation data is under exploited. MATERIALS AND METHODS: We analyzed CpG sites from two publicly available datasets, the TCGA-PAAD used as discovery set and the CPTAC-PDA as external test set. Single mutations and co-mutation of KRAS and TP53 genes were identified as targets, and differentially methylated CpG sites (DMC) were detected accordingly. We trained and validated Random Forest (RF) models to predict each target. Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision-Recall curve (AUPRC) were used as performance metrics. Then, we performed consensus clustering from the DMCs to identify novel patients' profiles. Finally, we trained and validated a combination of eXtreme Gradient Boosting (XGB) and tree models to select an epigenomic prognostic determinant. RESULTS: From 598 DMCs extracted, an RF model predicted KRAS and TP53 co-mutation on the external test set with AUROC of 0.77 and AUPRC of 0.87. The consensus clustering allowed us to identify 4 clusters (C1, C2, C3, and C4) of patients. The C4 cluster captured a subgroup of patients with favorable Overall Survival (OS) with respect to others. The XGB model perfectly predicted C4 vs other clusters on the discovery set. In both cohorts, patients were stratified into two risk groups according to methylation levels of cg16854533, individuated as the most important CpG site. CONCLUSION: We analyzed methylation data to develop a classifier for the TP53 and KRAS mutational status. Four prognostic clusters were pointed out and a prognostic model using a CpG site was validated in an independent cohort. Our results evidence that the proposed use of methylation data facilitates risk stratification for PDAC.

Humans↗

PubMed2026

Leveraging Interradiomic Feature Relationships for Enhanced Prediction of Distant Metastasis and Characterization of Heterogeneity in Head and Neck Cancer.

PURPOSE: Distant metastasis remains a major cause of treatment failure in head and neck (HN) cancer, highlighting the need for more accurate early risk stratification. This study developed and validated a deep radiomics framework to characterize tumor heterogeneity from pretreatment computed tomography (CT) images and improve prediction of distant metastasis-free survival (DMFS). METHODS AND MATERIALS: This multicenter study included 3421 patients with HN cancer from 4 cohorts across 12 institutions. Radiomics features were extracted from primary tumors and transformed into OmicsMaps, a structured representation that spatially organizes interfeature relationships to facilitate learning of complex prognostic patterns. A convolutional neural network was trained to derive prognostic signatures, which were integrated with key clinical variables to construct an OmicsMap-clinical fusion model for patient risk stratification. Model performance was assessed using the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) in the CT Images from Large Head and Neck Cohort (RADCURE), HEAD-NECK-RADIOMICS-HN1 (HN1), and Head-Neck-Positron Emission Tomography-Computed Tomography (HN-PET-CT) cohorts. Radiogenomic analyses using RNA-seq data were conducted in the Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) cohort to investigate biological characteristics associated with the imaging-defined risk groups. RESULTS: The OmicsMap achieved C-index values of 0.742, 0.768, and 0.671 in the RADCURE, HN1, and HN-PET-CT cohorts, outperforming the conventional radiomics approach by 5.40%-6.37%. Incorporating clinical variables further improved generalizability, yielding a C-index of 0.864 (HN1) and 0.730 (HN-PET-CT), with time-dependent AUC of 0.727-0.895. The fusion model consistently stratified patients into distinct high- and low-risk groups for both DMFS and overall survival across cohorts (P <.01). Radiogenomic analyses revealed enrichment of immune-related pathways in the low-risk group, whereas the high-risk group exhibited a more aggressive phenotype enriched for proliferation, hypoxia, and epithelial-mesenchymal transition pathways, along with a fibrosis-prone tumor microenvironment characterized by extracellular matrix remodeling. CONCLUSIONS: Modeling interradiomic feature relationships using the OmicsMap representation substantially improves CT-based prediction of DMFS and characterization of tumor heterogeneity in HN cancer, supporting precision risk stratification in clinical oncology.

Journal Article↗

PubMed2026

Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis.

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

Arachidonic acid metabolism↗

PubMed2026

KLF5-driven G6PD protects lung squamous cell carcinoma from ferroptosis by sustaining mitochondrial homeostasis and SLC7A11-dependent cystine uptake.

AIMS: Lung squamous cell carcinoma (LUSC) is a highly aggressive malignancy with limited therapeutic options. Ferroptosis has emerged as a promising antitumor strategy. However, the metabolic determinants governing ferroptotic vulnerability in LUSC remain incompletely understood. We investigated glucose-6-phosphate dehydrogenase (G6PD) in this context. MATERIALS AND METHODS: In vitro models using small interfering RNA (siRNA)-mediated G6PD depletion, together with pharmacological studies using 6-aminonicotinamide (6-AN) and LUSC xenograft models, were employed to investigate the underlying mechanisms. KEY FINDINGS: G6PD was markedly upregulated in LUSC, and analysis of the Cancer Genome Atlas lung squamous cell carcinoma (TCGA-LUSC) cohort showed that elevated G6PD expression was associated with advanced clinicopathological features and poorer overall survival. While ferroptosis inducers (erastin and RSL3) did not alter G6PD mRNA, they robustly increased G6PD protein during ferroptotic stress. Genetic or pharmacological inhibition of G6PD significantly sensitized LUSC cells to RSL3-induced ferroptosis, evidenced by enhanced lipid peroxidation, glutathione depletion, and ferrostatin-1-reversible cell death. Mechanistically, G6PD inhibition led to mitochondrial ferrous iron accumulation, elevated reactive oxygen species, impaired respiration, and activation of PINK1/Parkin-dependent mitophagy, which further exacerbated ferroptotic injury. In vivo, combined treatment with 6-aminonicotinamide and RSL3 markedly suppressed LUSC xenograft growth and enhanced biochemical markers of ferroptotic stress. Furthermore, G6PD protects cells by positively regulating the cystine/glutamate antiporter SLC7A11 to maintain redox homeostasis. Upstream, the oncogenic factor Krüppel-like factor 5 (KLF5) directly activates G6PD transcription. SIGNIFICANCE: Our findings identify a KLF5-G6PD-SLC7A11 axis as a critical metabolic safeguard against ferroptosis in LUSC. Targeting G6PD disrupts mitochondrial homeostasis, enhances mitophagy-dependent oxidative stress, and sensitizes tumors to ferroptotic therapy, highlighting a promising therapeutic strategy for LUSC.

Ferroptosis↗

PubMed2026

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

cancer cell lines↗

PubMed2026

A TIGIT nanotrapping-guided STING-activatable immunometabolic strategy overcomes innate immune silence and T cell exhaustion in breast cancer.

Breast cancer exhibits a profoundly immunosuppressive tumor microenvironment (TME), where innate immune silence prevents antigen sensing and persistent T cell exhaustion limits effector responses, rendering most immunotherapies ineffective. Clinical profiling of 1093 The Cancer Genome Atlas (TCGA) cases identified a glucose-fueled glutathione (GSH)-glutathione peroxidase 4 (GPX4)-dihydrolipoamide S-acetyltransferase (DLAT) axis as a dominant metabolic shield that suppresses oxidative stress, and thereby enforces both stimulator of interferon genes (STING) silence and CD8+ T cell exclusion. To dismantle this barrier, we developed an immunometabolic nanotherapy, GOx/ES-CO-LDH@TIGIT-Nanotrap (TNT). In acidic tumors, proton-driven layered double hydroxide (LDH) disassembly releases glucose oxidase (GOx) and extremely small cuprous oxide (ES-CO). GOx depletes glucose and nicotinamide adenine dinucleotide phosphate (NADPH) to induce disulfidptosis, while ES-CO releases cuprous ions (Cu+) that trigger cuproptosis via binding to lipoylated mitochondrial proteins. Their mutual biochemical amplification produces a cycloacclerated disulfidptosis-cuproptosis cascade that collapses the GSH-GPX4-DLAT axis and restores STING activation. Meanwhile, the macrophage-derived T cell immunoreceptor with Ig and ITIM domains (TIGIT) Nanotrap sequesters CD155 to prevent T cell suppression. Together, this coordinated innate reactivation and adaptive rescue converts immune-cold tumors into STING-inflamed and T cell responsive lesions.

Female↗

PubMed2026

SLC1A5 and NUMA1 are potential regulators and therapeutic targets of ferroptosis in diffuse large B-cell lymphoma.

BACKGROUND: Ferroptosis, a form of regulated cell death driven by iron-dependent lipid peroxidation, has emerged as a potential therapeutic target in various cancers, including diffuse large B-cell lymphoma (DLBCL). This study aimed to identify and characterize ferroptosis-related panel genes with prognostic value in DLBCL. METHODS: Transcriptomic data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) in DLBCL samples. Gene set variation analysis (GSVA) and network topology analysis were performed to identify key ferroptosis-related genes. Lasso regression was utilized to construct a prognostic model based on the identified panel genes. In vitro experiments, including gene silencing, overexpression, and ferroptosis induction, were conducted to evaluate the functional roles of the identified genes, NUMA1 and SLC1A5, in DLBCL cells. RESULTS: A panel of ferroptosis-related genes with prognostic value, including NUMA1 and SLC1A5, was identified in DLBCL samples. Silencing SLC1A5 or overexpressing NUMA1 in DLBCL cells enhanced sensitivity to ferroptosis inducers, increased intracellular labile iron and lipid peroxidation levels, promoted mitochondrial damage, and modulated the expression of key ferroptosis markers. Furthermore, SLC1A5 silencing or NUMA1 overexpression augmented radiation-induced ferroptosis in DLBCL cells. CONCLUSION: NUMA1 and SLC1A5 are potential ferroptosis regulators and therapeutic targets in DLBCL. Silencing the ferroptosis-suppressive transporter SLC1A5 or restoring NUMA1 expression promotes lipid peroxidation and ferroptotic cell death, thereby sensitizing DLBCL cells to ferroptosis and enhancing radiosensitivity-providing a rationale for novel ferroptosis-based therapeutic strategies.

Humans↗

PubMed2025

Clinicopathologic and genomic analyses of SMARCA4-mutated non-small cell lung carcinoma implicate the needs for tailored treatment strategies.

BACKGROUND: The clinicopathologic and therapeutic significance of SMARCA4 mutation in non-small cell lung carcinoma (NSCLC) remains unclear. METHODS: We retrieved 575 NSCLC cases from the clinical target sequencing cohort (N = 2157) to compare the clinicopathologic characteristics of groups subclassified based on the presence of truncated or non-truncated SMARCA4 mutations (SMARCA4-truncated, SMARCA4-non-truncated, and SMARCA4-wild type [WT]). The differences in gene expression profiles between these groups were evaluated using the TCGA-LUAD dataset. RESULTS: Fifty (2.3%) SMARCA4-truncated and 63 (2.9%) SMARCA4-non-truncated NSCLCs were identified. The majority of SMARCA4-truncated NSCLCs were present in male smokers (94.0%) and pathologically diagnosed as adenocarcinoma (76.0%). The SMARCA4-truncated group showed rare targetable driver alterations with a higher tumor mutation burden than the SMARCA4-WT group. Gene expression profile analysis revealed that cancer/testis antigen (CTA) expression was enriched in the SMARCA4-truncated group, with up to 57% of the cases displaying immunoreactivities for MAGEA4, CT45A, and/or PRAME. The SMARCA4-non-truncated group showed heterogeneous clinicopathologic, genomic, and immunohistochemical features that fell between SMARCA4-truncated and WT groups. Both SMARCA4-truncated and non-truncated groups showed significantly poor prognosis with pemetrexed-platinum chemotherapy, yet there was no significant difference in survival following immune checkpoint inhibitor monotherapy. CONCLUSION: SMARCA4-truncated NSCLC represents a variant of driver-negative NSCLC, mainly occurring in male smokers with poorly differentiated adenocarcinoma histology. In contrast, SMARCA4-non-truncated NSCLC indicates a heterogeneous subpopulation, exhibiting intermediate characteristics between the SMARCA4-truncated and SMARCA4-WT groups. While showing poor response to pemetrexed-platinum chemotherapy, increased CTA expression could be a novel therapeutic target in SMARCA4-mutated NSCLCs.

Humans↗

PubMed2025

Targeting IDH2 promotes antitumor immunity through epigenetic activation of cGAS-STING pathway.

Reductive carboxylation is critical for the proliferation of cancer cells and the differentiation of T cells. However, the role of this reaction in cancer cell-mediated tumor immunity remains unclear. Analysis of TCGA database showed a negative correlation between IDH2 expression and the presence of CD8+ T cells in lung and breast cancers, whereas IDH1 expression didn't show such a correlation. Further GSEA analysis revealed a significant enrichment of immune-related genes within IDH2-associated genes, specifically those in the type Ⅰ interferon pathway. In lung cancer cells, the depletion of IDH2 expression indeed could induce the activation of the immune-related and specially type Ⅰ interferon pathway. Targeting IDH2 with shRNA or its inhibitor AGI-6780 caused an increase in intracellular α-ketoglutarate concentration and a decrease in ATP and SAM levels, leading to a reduction in the methylation of STING promoter and elevated levels expression of STING. The increase of STING expression underlies the activation of type I interferon pathway observed in IDH2 compromised tumor cells and increased defense responses in the tumors in mice. These results identify IDH2 as a potential target to enhance cancer immune therapy.

Humans↗

PubMed2026

Noncanonical Transcription and Splicing Shape the Colorectal Cancer Immunopeptidome in MSI and MSS Tumors.

Treatment with immune checkpoint inhibitors in colorectal cancer (CRC) has largely benefited patients with microsatellite instability-high (MSI-H) and not the larger proportion of patient with microsatellite-stable (MSS) tumors. This clinical dichotomy has fueled the view that high mutational burden is the dominant driver of tumor immunogenicity and that MSS CRC fails to respond because it is "antigen poor". To directly test this premise and define the origins of presented tumor antigens, we integrated HLA class I immunopeptidomics and matched RNA-seq from 26 primary CRC tumors spanning MSI-H and MSS subtypes. Using patient-specific canonical and cancer-specific proteogenomic databases, we identified 115,292 unique major histocompatibility complex (MHC)-associated peptides (MAPs) across 61 HLA alleles, with a mean of 9292 MAPs per tumor and no significant difference in MAP counts between MSI-H and MSS tumors. In toto, we identified 266 tumor antigens, all coded by unmutated genomic sequences, comprising 70 aberrantly expressed tumor-specific antigens (aeTSAs) and 196 tumor-associated antigens (TAAs). In our cohort, MSS tumors presented more TAAs and a comparable number of aeTSAs per tumor relative to MSI-H tumors. In TCGA-COAD stratified analyses (483 tumors), MSS tumors yielded more presentable aeTSAs and TAAs per patient than MSI-H tumors. Across both subtypes, aeTSAs arose predominantly from intronic translation, UTR usage, retroelement activation, and germline-like transcription, including recurrent aeTSAs from PIWIL1, L1TD1, and endogenous retroviral loci. Together, these data demonstrate that MSS CRC is not antigen poor and highlight noncanonical translation as a major, previously underappreciated contributor to the CRC immunopeptidome.

Humans↗

PubMed2026

Extracellular Vesicles From Glioblastoma Cells Reflect 2D vs. 3D Culture Adaptation and Resistance to Temozolomide.

Glioblastoma (GBM) is an aggressive brain tumor marked by extensive heterogeneity, resistance to therapy, and dismal prognosis. Extracellular vesicles (EVs) have emerged as key players in GBM biology, mediating intercellular communication and therapy adaptation. However, the exact functions and molecular impact of EVs in GBM remain incompletely understood. In this study, we performed a comparative proteomic analysis of U87MG GBM cells grown in two-dimensional (2D) monolayers and three-dimensional (3D) spheroids following temozolomide (TMZ) treatment, alongside characterization of EVs derived from both culture systems. 3D-spheroids secreted more EVs of smaller size and exhibited a more TMZ-resistant, stem-like proteome under TMZ-induced genotoxic stress. In contrast, 2D cell cultures demonstrated greater proteome remodeling, with EVs enriched in protein families involved in DNA repair, oxidative stress adaptation, and methylation processes. Notably, several methyltransferases were decreased intracellularly but selectively retained in EVs, suggesting active sorting to influence the tumor microenvironment or modulate epigenetic states in recipient cells. EVs also carried adhesion molecules and signaling proteins linked to migration, invasion, and Wnt pathway activation, as well as metabolic enzymes connecting serine metabolism and redox control to TMZ resistance. Mapping EV and cellular proteomes onto The Cancer Genome Atlas (TCGA) dataset identified prognostic protein families associated with either poor or favorable patient outcomes. Our data demonstrate that EV cargo composition mirrors TMZ-induced phenotypic adaptation and reveals molecular mechanisms underlying therapeutic resistance. These EV-associated signatures may serve as clinically actionable biomarkers for patient stratification and offer potential targets to overcome chemoresistance in GBM.

Humans↗

PubMed2026

sWGS Identifies a Copy-Number-High Subset of TP53-mutated Multiple-Classifier Endometrial Carcinomas With Adverse Clinicopathological Features.

TP53-mutated "multiple-classifier" endometrial carcinomas represent a diagnostically challenging subgroup within current molecular classification algorithms. Although these tumors are assigned to POLE-mutated or mismatch repair-deficient categories according to current ESGO/FIGO-based algorithms, their biological heterogeneity remains incompletely characterized. Herein, we retrospectively analyzed TP53-mutated multiple-classifier endometrial carcinomas identified through routine molecular profiling at our institution between 2022 and 2025 using an integrated histopathological, immunohistochemical, targeted sequencing, and shallow whole-genome sequencing approach. Copy-number alteration-high (CNA-high) status was defined as ≥5 large-scale genomic alterations, corresponding to copy-number gains or losses ≥3 Mb within a single chromosomal arm excluding whole-arm alterations. Among 33 analyzable TP53-mutated multiple-classifier endometrial carcinomas, sWGS identified 12 CNA-high tumors (36.4%) and 21 CNA-low tumors (63.6%). CNA-high tumors were more frequently non-endometrioid, high-grade, and advanced-stage according to FIGO 2023. They showed higher TP53 variant allele frequencies (VAF) and higher TP53 VAF-to-tumor-cellularity ratios. After a median follow-up of 12.8 months, recurrences (6/33; 18.2%) and disease-related deaths (3/33; 9.1%) were observed in the CNA-high subgroup, whereas no recurrence or disease-related death was observed among CNA-low patients. These findings indicate that TP53-mutated multiple-classifier endometrial carcinomas comprise biologically distinct subsets that are not fully captured by current 4-tier TCGA-based molecular classification and ESGO-based risk stratification. In this cohort, sWGS identified a CNA-high group with adverse clinicopathological features and clinical events suggesting a potentially more aggressive clinical course. Integration of genome-wide copy-number profiling may therefore refine the biological interpretation of TP53 alterations in multiple-classifier endometrial carcinomas and warrants validation in larger multicenter cohorts.

TP53↗

PubMed2026

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 688 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

DNA methylation↗

PubMed2026

Pan-cancer landscape of APEX1 expression and genomic alterations: Associations with clinical outcomes and functional validation in lung cancer.

This pan-cancer study systematically evaluated APEX1 expression, genomic alteration status, and prognostic value across 13,270 tumors and 2544 normal tissues from TCGA and GTEx. APEX1 overexpression was prominent in bladder, breast, and colon adenocarcinomas and correlated with advanced tumor stages and survival outcomes in a cancer-type-specific manner, with unfavorable associations in selected tumor contexts and an opposite favorable association in KIRC. Low-frequency APEX1 genomic alterations require cautious interpretation and may be associated with adverse outcomes in selected tumor contexts. Statistical methods including t-tests, ANOVA, Cox regression, Benjamini-Hochberg false-discovery rate correction for cohort-wise OS analyses, and log-rank tests were used to correlate APEX1 expression or alteration status with survival outcomes. The findings suggest that APEX1 expression and low-frequency genomic alterations are associated with tumor progression and worse patient outcomes in selected cancer contexts. This study supports APEX1 as a candidate prognostic indicator requiring further validation and suggests its potential value for future clinical stratification research. The pan-cancer approach improves contextual breadth, and the large sample size adds robustness. The observed relevance of APEX1 across malignancies supports further evaluation of its value for risk stratification and treatment planning. Future prospective studies are needed to validate its clinical utility. This work contributes to the growing field of DNA repair-related biomarkers and may inform future studies of APEX1-targeted therapeutic strategies.

Apurinic/Apyrimidinic Endonuclease 1↗

PubMed2026

Dynamic evolution of chaperone-mediated autophagy is associated with tumor microenvironment remodeling and prognostic stratification in lung adenocarcinoma: insights from single-cell transcriptomics, ensemble machine learning, and experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) shows prognostic heterogeneity, and tumor-node-metastasis (TNM) staging is limited for individualized management. Chaperone-mediated autophagy (CMA) maintains proteostasis, but its role during adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA)-invasive adenocarcinoma (IAC) progression remains unclear. METHODS: Single-cell RNA sequencing (scRNA-seq) data from GSE189357 and bulk transcriptomes from The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus (GEO) cohorts were integrated. CMA activity, cell-cell communication, weighted gene co-expression network analysis (WGCNA), tumor-normal differential expression, machine-learning survival modeling, tumor microenvironment (TME) features, drug sensitivity, and EPC1 function were analyzed. RESULTS: CMA-high tumor epithelial cells increased from AIS (58.1%) to MIA (65.7%) but declined in IAC (44.4%; p < 0.001). CMA-low cells preferentially received fibroblast-derived extracellular matrix cues. A CMA-negatively correlated module identified 69 core genes. Random survival forest (RSF) performed best among 117 machine-learning combinations (mean concordance index > 0.873). High-risk patients had worse survival across cohorts, and the risk score was independently associated with overall survival (hazard ratio = 16.013, 95% confidence interval: 9.579-26.768, p < 0.001). High-risk tumors showed proliferative activation and M0 macrophage enrichment, whereas low-risk tumors showed stronger immune-related signaling. EPC1 overexpression suppressed malignant phenotypes in A549 cells. CONCLUSION: CMA dynamics are associated with stromal and immune remodeling during LUAD progression. A CMA-based model provides robust prognostic stratification and may offer a basis for future TME-guided studies.

Chaperone-mediated autophagy↗

PubMed2026

Unveiling the power of TIIC: A prognostic tool for esophageal adenocarcinoma.

BACKGROUND: Esophageal adenocarcinoma (EAC) remains a lethal malignancy with limited prognostic tools for guiding immunotherapy. Tumor-infiltrating immune cells (TIICs) play a critical role in EAC prognosis and treatment response. METHODS: We integrated single-cell RNA sequencing and bulk transcriptome data from TCGA and GEO databases. TIIC-specific RNAs were identified via tissue specificity index calculation combined with machine learning feature selection. Twenty machine learning algorithms were benchmarked to construct an optimal TIIC signature score (TIIC-Score) based on the comprehensive C-index. Immunotherapy response, genomic mutation, and copy number variation were analyzed. Summary-data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (MR) were performed to explore genetic associations. Core prognostic TIIC-related genes were functionally validated in esophageal cancer cell lines through loss-of-function assays. RESULTS: The TIIC-Score demonstrated robust prognostic value for 1-, 2-, and 3-year overall survival across multiple cohorts, outperforming 22 published models. High TIIC-Score was associated with poor survival and increased chromosomal instability. Mutation profiling revealed high frequencies of TP53 (78.2%), TTN (48.7%), and SYNE1 (30.8%). MR analysis identified a significant association between gastro-oesophageal reflux and EAC risk at SNP rs8130507. Functionally, CCNI was upregulated in esophageal cancer cells, and its knockdown suppressed malignant phenotypes while promoting apoptosis, supporting its pro-tumorigenic role. CONCLUSION: The TIIC-Score provides a novel prognostic framework for EAC that effectively stratifies patient risk and may help identify individuals most likely to benefit from immunotherapy.

Esophageal adenocarcinoma↗

PubMed2026

Integrated single-cell and bulk transcriptomic analysis identifies a novel senescent fibroblast subtype associated with poor prognosis in acral melanoma.

BACKGROUND: Acral melanoma (AM) exhibits significant intratumoral heterogeneity, but its tumor microenvironment (TME) and immune regulation remain unclear. This study aims to dissect TME heterogeneity and establish a prognostic model based on key cell subpopulations. METHODS: We collected AM single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). Unsupervised clustering, CellChat, and Scissor analysis were performed to characterize cellular heterogeneity, cell-cell communication, and prognosis-related cell subpopulations. Kaplan-Meier analysis was used to assess the prognostic value of key genes, which were further validated by multiplex immunohistochemistry (mIHC). RESULTS: In AM, Mel_C2, C7, and C9 with high SEMA6A and KIT expression were strongly linked to poor prognosis. We further identified a senescent fibroblast subpopulation (sCAF_CDKN2A) characterized by high fibroblast senescence signature (FSS) scores. Integrating Scissor analysis of fibroblast subtypes with bulk prognostic data, we identified COL3A1, VCAN, and KIT as prognosis-associated genes upregulated in poor-outcome-related fibroblast subsets. Cell-cell communication analysis revealed that sCAF_CDKN2A engages in an immunosuppressive network, interacting with regulatory T cells (Tregs) via MIF signaling and receiving signals from exhausted CD8+ T cells through PPIA-BSG interactions. Using transcription factor expression patterns from these fibroblast subtypes, we constructed a prognostic model that effectively stratified patients into distinct risk groups with significant differences in overall survival (OS). mIHC confirmed significantly higher protein levels of SEMA6A and COL3A1 in tumor tissues compared to matched normal tissues. CONCLUSIONS: We established a novel prognostic model for AM and identified sCAF_CDKN2A as an immunosuppressive senescent fibroblast subpopulation driving poor prognosis.

Acral melanoma↗

PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗