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PubMed2026

Integrative machine learning and transcriptomic analysis reveals molecular mechanisms underlying low survival rate in larval Chinese Bahaba (Bahaba taipingensis).

Chinese Bahaba (Bahaba taipingensis) is a Class I protected marine fish endemic to China. Low larvae survival during artificial breeding severely hinder population recovery. To investigate the molecular mechanism of high mortality in larval fish, this study performed RNA-seq on liver from naturally deceased (ND) and mass-dead (MD) individuals, combined with least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithms to screen for core signature genes. A total of 873 differentially expressed genes (DEGs) were identified, including 112 upregulated and 761 downregulated genes. GO and KEGG enrichment analyses revealed significant enrichment in amino acid metabolism disorders, one‑carbon folate pool impairment, PPAR signaling abnormalities, ECM-receptor interaction, focal adhesion pathway, indicating widespread metabolic suppression accompanied by extracellular matrix remodeling and signaling disturbances in the livers of MD fish. MAD pre-filtering combined with dual machine learning algorithms yielded 18 robust core signature genes, among which SLC38A4, MMP1, FADD, FKBP5, and APOB were consistently identified as high-frequency core genes by both algorithms. SLC38A4 exhibited the highest importance score in the RF model and was significantly downregulated, making it the primary molecule distinguishing ND from MD phenotypes. ROC curve analysis showed that both models achieved an AUC of 1.000 (95% CI lower bound: 0.610), confirming the precise discriminatory ability of the core genes. GSEA further demonstrated significant enrichment of this core gene set in ND samples. This study provides the first systematic elucidation of the molecular mechanisms underlying liver dysfunction in low survival rate B. taipingensis, characterized by amino acid transport impairment, metabolic reprogramming, and structural remodeling, offering theoretical foundations for health assessment, early mortality risk warning, and artificial breeding conservation of this species.

Animals↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2025

Identification and validation of prognostic genes associated with mitochondrial nuclear genes in gastric cancer.

Mitochondrial-related nuclear genes (MNGs) have shown great importance in cancer diagnosis and prognosis, but their role in gastric cancer (GC) remains unclear. GC-related transcriptome data from the gene expression omnibus and cancer genome atlas databases were analyzed to identify differentially expressed MNGs. A prognostic risk model was constructed through univariate Cox and least absolute shrinkage and selection operator regression, validated by Kaplan-Meier (K-M) survival curve and receiver operating characteristic curve. This was followed by immune infiltration analysis, independent prognostic analysis, functional enrichment analysis, drug sensitivity analysis, drug prediction, molecular docking and construction of regulatory networks. Three prognostic genes (ATP8A2, COX15 and TARS2) were identified. The expression of TARS2 and COX15 was positively correlated with CNV, while ATP8A2 was unaffected. The risk model and nomogram, integrating risk score and clinicopathological factors, exhibited excellent predictive performance. A significant correlation was observed between prognostic genes and differential immune cells, such as T cells, B cells, and NK cells. BMS-754807, Gefitinib, JQ1, Lapatinib, and Sapitinib exhibited significant differences in sensitivity between the high-risk group and the low-risk group. The results of molecular docking showed TP8A2 has stable binding ability with cytosine, COX15 with indomethacin, and TARS2 with bisacodyl. RT-qPCR revealed downregulation of ATP8A2 and upregulation of COX15 and TARS2 in GC samples. MNGs, including ATP8A2, COX15, and TARS2, demonstrated significant associations with immune infiltration, CNV, and prognostic outcomes of GC.

Humans↗

PubMed2026

Construction of molecular signatures based on the co-expression network of NECSO-related gene TRPM4 and its prognostic value in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) demonstrates significant prognostic variability that is not entirely accounted for by traditional staging systems. Necrosis by sodium overload (NECSO) is an emerging programmed cell death pathway, but its clinical relevance in HCC remains undefined. Therefore, this study aimed to identify TRPM4-associated core genes, develop and validate a prognostic signature, and investigate its relationship with the tumor immune microenvironment, tumor mutational burden, and single-cell expression patterns in HCC. METHODS: We integrated transcriptomic, clinical, and mutational datasets from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) (n=421) and Gene Expression Omnibus (GEO) cohorts (n=115) to identify genes co-expressed with TRPM4-a key NECSO mediator-and those differentially expressed in HCC. A prognostic signature was developed using least absolute shrinkage and selection operator (LASSO)-Cox regression and validated through survival analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox regression analysis. The immune landscape was characterized using CIBERSORT, somatic mutation data were used to calculate tumor mutational burden (TMB) and assess its correlation with the risk score, and single-cell RNA sequencing (scRNA-seq) resolved cell-type-specific expression patterns. RESULTS: From 294 TRPM4-associated core genes, we identified an 11-gene signature (BRSK1, MMP1, GRIN2D, GP6, MYOM2, N4BP3, CCDC112, TSEN54, MAP3K9, SPP1, B3GNT4) that independently predicted overall survival (OS) (hazard ratio =5.419, P<0.001) with areas under the curve (AUCs) of 0.779, 0.693, and 0.701 at 1, 3, and 5 years. These values were superior or comparable to conventional clinicopathologic variables after direct comparison. High-risk patients exhibited an immunosuppressive microenvironment, characterized by enrichment of M0 macrophage, a higher M2/M1 ratio (P<0.001) and distinct immune checkpoint profiles. When integrated with TMB, the prognostic stratification was further refined: high-TMB/high-risk patients had poorest outcomes (median OS, 15.3 months), while low-TMB/low-risk patients had the most favorable survival (median OS, 68.7 months). Single-cell analysis revealed that MMP1 was induced in cancer-associated fibroblasts (CAFs) and SPP1 was downregulated in macrophages, single-cell risk scores confirmed TAFs and macrophages as the main contributors to the prognostic model. CONCLUSIONS: The TRPM4-centered 11-gene signature provides robust and independent prognostic stratification in HCC by integrating immune, mutational, and single-cell features. This signature serves as a potential tool for prognostic evaluation and may help inform immunotherapeutic strategies for HCC.

Hepatocellular carcinoma (HCC)↗

PubMed2026

A novel lactylation-related gene signature deciphers the immunosuppressive microenvironment and stratifies precision therapy in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of cancer mortality, largely due to the heterogeneity of the tumor microenvironment (TME) and the limited efficacy of immunotherapy in microsatellite stable (MSS) tumors. Histone lactylation, a post-translational modification derived from the Warburg effect, serves as a critical bridge linking metabolic reprogramming to gene regulation and immune evasion; however, its specific prognostic value and clinical implications in CRC remain to be fully elucidated. METHODS: In this study, we systematically analyzed transcriptome profiling data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, supplemented by single-cell RNA sequencing (scRNA-seq) analysis and Human Protein Atlas (HPA) protein-level validation. By integrating univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and multivariate Cox regression, we constructed a novel lactylation-related gene (LRG) risk signature. We extensively evaluated the association between this risk signature and patient prognosis, immune infiltration patterns, somatic mutations, and therapeutic sensitivity. RESULTS: A robust 9-gene prognostic signature (DHRS7, SPR, MBD2, RBM17, CSRP2, S100A4, TMSB4X, TKT, COPS4) was identified and corroborated at the protein level. Patients with high risk scores exhibited significantly worse overall survival (OS) across the training and two independent validation cohorts. Immunogenomic and scRNA-seq analyses revealed that high-risk tumors were characterized by an immunosuppressive and stromal-dense microenvironment-with stromal cells exhibiting the highest lactylation risk scores-enriched with regulatory T cells (Tregs), and frequently harbored PIK3CA mutations. Differential expression analysis indicated that this immune exclusion is structurally maintained by enriched extracellular matrix (ECM) organization and TGF-β signaling. Conversely, low-risk tumors displayed an inflamed phenotype with active antitumor immunity. Pharmacogenomic prediction identified distinct therapeutic stratifications: low-risk patients exhibited significant sensitivity to standard chemotherapeutics (fluorouracil, oxaliplatin) and EGFR/HER2 inhibitors (e.g., lapatinib, erlotinib). In contrast, high-risk patients showed specific vulnerabilities to novel targeted agents, including PI3K pathway inhibitors (TG-100-115, XL765), microenvironment-modulating agents (sildenafil, GANT-61), and epigenetic inhibitors (UNC0638). CONCLUSION: We established a novel lactylation-related risk signature that effectively stratifies CRC patients by prognosis and TME characteristics. By elucidating the crosstalk between metabolic dysregulation, stromal barriers, and immune exclusion, this study provides potential biomarkers and stratified therapeutic strategies-ranging from standard chemotherapy to targeted metabolic and stromal interventions-to optimize precision medicine for CRC patients.

Colorectal cancer↗

PubMed2026

A per- and polyfluoroalkyl substances-based gene signature links prognosis to immune landscapes in thyroid cancer.

BACKGROUND: Thyroid cancer (THCA) is the most common endocrine malignancy with a rising global incidence and significant heterogeneity. Although per- and polyfluoroalkyl substances (PFAS) exposure is linked to thyroid dysfunction, the prognostic value of per- and polyfluoroalkyl substances-related genes (PFASRGs) and their role in the tumor immune microenvironment (TME) remain poorly understood. This study aims to systematically screen key PFASRGs and evaluate their prognostic value as biomarkers for THCA. METHODS: Utilizing The Cancer Genome Atlas (TCGA)-THCA transcriptomic data and PFASRGs, we constructed a prognostic model through differential expression analysis, univariate and multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO). The model's robustness was validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, and clinical nomograms. Furthermore, the TME, immunotherapy response, and drug sensitivities were systematically evaluated. Distinct molecular landscapes were characterized by stratifying the cohort via unsupervised consensus clustering analysis. RESULTS: The eight-gene prognostic model demonstrated robust performance, with area under the curve (AUC) values exceeding 0.85 across all validation cohorts. High-risk patients exhibited significantly shorter overall survival and an "inflamed" TME characterized by high immune scores and checkpoint expression. In contrast, the therapeutic efficacy of anti-programmed death-ligand 1 (PD-L1) agents was more pronounced in the low-risk category, as evidenced by a superior objective response. Furthermore, distinct molecular subtypes and risk-specific sensitivities to targeted agents, such as sorafenib and sunitinib, were identified, highlighting the model's clinical utility for personalized treatment. CONCLUSIONS: We established a novel THCA prognostic framework based on eight PFASRGs. This model exhibits superior performance in risk stratification, effectively distinguishing cohorts with divergent clinical trajectories, unique immune microenvironment features, and varied therapeutic responses. Our findings provide a powerful predictive tool for refining prognostic evaluation and facilitating the implementation of personalized management strategies for THCA patients.

Per- and polyfluoroalkyl substances-related genes ↗

PubMed2026

Primary Tumor Epigenetic and Transcriptomic Alterations Associated with Nodal Burden and Metastatic Risk in ER+/HER2- Breast Cancer.

De-escalation of axillary surgery has resulted in the loss of pathologic nodal information, yet the extent of lymph node involvement remains an important determinant of treatment decisions in estrogen receptor-positive (ER+)/HER2- disease. We examined whether primary tumors differed molecularly according to the extent of this regional dissemination. Genome-wide DNA methylation profiling of primary ER+/HER2- tumors from 47 patients with pN1 (n = 29) vs. >pN1 (n = 18) disease showed differences concentrated at promoters of developmental and cell-adhesion genes. By integrating methylomes with transcriptomes from the TCGA-BRCA cohort (n = 148) and clinical outcomes from KM Plotter (RFS, n = 1154; OS, n = 442; DMFS, n = 423), we identified four genes (ARL10, RIC3, CXCL14, KCNH2) showing concordant molecular and clinical associations, from which we derived the Lymph-node Involvement Outcome Numerator (LION) score. Lower LION scores were observed in metastatic lesions from the AURORA US cohort (n = 45). In SCAN-B (n = 3969), lower scores were associated with shorter distant recurrence-free intervals (HR = 0.38; 95% CI 0.23-0.62); this association persisted after adjustment for age, nodal and tumor category but was lost after adjustment for histological grade (HR = 0.83; 95% CI 0.48-1.44), indicating that the score and grade capture overlapping biology. These findings suggest that primary tumors already display coordinated epigenetic and transcriptional alterations associated with the extent of metastatic dissemination.

Humans↗

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

Age-Associated Four-Gene Prognostic Signature in Breast Cancer.

BACKGROUND: Young-onset breast cancer is associated with inferior disease-free survival (DFS), but the contribution of additional molecular heterogeneity remains unclear. AIMS: To identify an exploratory age-associated gene expression signature linked to recurrence-related outcomes and evaluate its prognostic association. METHODS AND RESULTS: We analyzed clinicopathological and RNA-sequencing data from 821 patients with Stages I-III invasive ductal or lobular carcinoma in The Cancer Genome Atlas, including 142 patients aged ≤ 45 years. Genes associated with both age and DFS were screened, followed by LASSO-Cox and stepwise multivariable Cox regression. A four-gene signature (Sig4: C4orf14 [NOA1], LINC01124, ZNF704, and AGFG2) was identified. Young patients had significantly worse DFS than older patients, whereas overall and disease-specific survival did not differ significantly. After adjustment for clinicopathological factors, young age remained associated with worse DFS. Following inclusion of the continuous Sig4 score, the age association was attenuated and no longer statistically significant, while Sig4 remained independently associated with worse DFS. Sig4-high tumors were enriched for proliferation, cell-cycle, DNA-repair, metabolic, and stress-response pathways. In METABRIC, the fixed TCGA-derived Sig4 score was associated with worse relapse-free survival in the overall cohort but not in patients aged ≤ 45 years. CONCLUSION: Sig4 is an exploratory age-associated four-gene signature with potential general prognostic relevance in breast cancer. Its utility for risk stratification specifically in young-onset breast cancer was not externally validated and requires confirmation in independent prospective cohorts enriched for young patients.

Humans↗

PubMed2026

Prognostic significance of DNA damage response-related markers in esophageal squamous cell carcinoma using machine learning approaches.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) lacks reliable prognostic biomarkers. Homologous recombination deficiency (HRD) has been implicated in genomic instability across multiple cancers, but its prognostic significance in ESCC remains unexplored. This study aimed to evaluate HRD score as a prognostic biomarker and develop a machine learning-based predictive model for ESCC. METHODS: Transcriptomic and clinical data from 78 ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and randomly split into training (70%) and test (30%) cohorts. Prognostic models were constructed using 112 machine learning algorithm combinations based on DNA damage response (DDR)-related genes. Gene set enrichment analysis (GSEA), somatic mutation profiling, and immune cell infiltration estimation via CIBERSORT were performed to characterize HRD-associated molecular features. RESULTS: High HRD scores were significantly associated with poorer overall survival (P<0.05). Among 112 algorithm combinations, the survival support vector machine (Survival-SVM) model demonstrated optimal performance [training concordance index (C-index): 0.741; test C-index: 0.708], identifying six hub genes: PARP1, MBD4, TELO2, NSMCE3, SMUG1, and BABAM1. A nomogram incorporating risk score (RS) and clinical variables achieved strong predictive accuracy for 1- to 3-year survival [area under the curve (AUC) >0.7]. High-HRD tumors exhibited distinct mutational patterns (TP53 and TTN) and enriched glutathione metabolism and cytochrome P450 pathways. Immune infiltration analysis revealed significant differences in plasma cell and neutrophil infiltration between risk groups (P<0.05), suggesting HRD-associated immune microenvironment remodeling. CONCLUSIONS: We developed a novel HRD-based prognostic model incorporating six DDR-related genes that demonstrates robust predictive performance in ESCC. HRD score is identified as an independent prognostic factor associated with genomic instability, immune microenvironment alterations, and clinical outcomes. These findings provide a theoretical basis for personalized treatment strategies, including potential applications of PARP inhibitors and immunotherapy in ESCC.

Esophageal squamous cell carcinoma (ESCC)↗

PubMed2026

Integrative Multi-Omics Analysis Identifies Thrombosis-Associated Molecular Features Linked to Germline Susceptibility and Immune Cell Communication in Gastric Cancer.

Emerging evidence indicates that coagulation-related molecular programs are associated with thrombosis, tumor progression, and molecular dysregulation in gastric cancer (GC). However, thrombosis-associated molecular features in GC and their potential links to inherited susceptibility remain insufficiently understood. Integrated analyses of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were performed to identify thrombosis-associated genes and establish a machine learning-based prognostic signature. Genome-wide association study (GWAS), expression quantitative trait loci (eQTL), transcriptome-wide association study (TWAS), and Mendelian randomization (MR) analyses were conducted to investigate susceptibility-associated transcriptional programs in GC. Functional assays were used to evaluate candidate genes associated with malignant phenotypes. Single-cell RNA sequencing (scRNA-seq) and cell-cell communication analyses were further performed to characterize cell-type-specific expression patterns and potential intercellular interactions. A total of 22 differentially expressed thrombosis-associated genes were identified, and a prognostic signature comprising 14 genes was established. The signature stratified patients into high- and low-risk groups and showed prognostic performance in both the training and validation cohorts. Integrative GWAS, eQTL, and TWAS analyses identified susceptibility-associated transcriptional programs that were positively correlated with the thrombosis-associated risk score. Silencing ACTN2 and CRYAB significantly reduced GC cell migration and invasion. scRNA-seq analysis revealed relatively high CRYAB expression in neutrophils, and CellChat analysis suggested potential neutrophil-B cell interactions involving COLLAGEN-related signaling. This integrative multi-omics study identified a thrombosis-associated molecular signature linked to prognosis and germline susceptibility-associated transcriptional programs in GC. ACTN2 and CRYAB may represent candidate genes associated with GC cell migration and invasion, while single-cell analysis suggested potential immune-related communication features.

Humans↗

PubMed2026

A CFH- and SPINT2-based prognostic signature for cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant tumor with a poor prognosis, and reliable biomarkers for postoperative risk stratification remain limited. This study aimed to develop and validate a CFH- and SPINT2-based prognostic signature to support postoperative risk stratification and inform adjuvant therapy selection in CCA through integrative machine learning and single-cell transcriptomics. METHODS: Differentially expressed genes were screened from GSE26566. Integrative machine learning (least absolute shrinkage and selection operator-Cox, random forest, and univariate Cox regression) was performed in the training cohort (GSE89749; n=115) to construct a risk model, which was externally validated in two independent cohorts: cohort 1 (E-MTAB-6389; n=75) and cohort 2 [The Cancer Genome Atlas Cholangiocarcinoma (TCGA-CHOL) data set; n=36]. Systematic analysis was conducted and included examinations of immune infiltration [via single-sample gene set enrichment analysis (ssGSEA)], pathway enrichment (via hallmark GSEA), cellular localization (via single-cell RNA sequencing), and drug sensitivity (via the Genomics of Drug Sensitivity in Cancer 2 database). RESULTS: Two genes, CFH and SPINT2, were identified and incorporated into a prognostic risk score. High-risk patients in the training cohort had a significantly worse overall survival (log-rank P=0.02). External validation was performed in two independent cohorts. In validation cohort 1, the risk group was an independent prognostic factor [hazard ratio =2.27, 95% confidence interval (CI): 1.18-4.37; P=0.01]. In validation cohort 2, the model demonstrated acceptable discriminative ability (concordance index =0.721; 3-year area under the curve =0.692). The high-risk group exhibited an immunosuppressive microenvironment characterized by increased infiltration of macrophages and myeloid-derived suppressor cells, along with the activation of epithelial-mesenchymal transition, inflammatory response, and NF-κB signaling pathways. Single-cell analysis revealed a cell-type-specific expression pattern: CFH was predominantly expressed in fibroblasts, while SPINT2 was mainly expressed in malignant cells. Drug sensitivity analysis demonstrated that the high-risk group was more sensitive to gemcitabine, cisplatin, poly(ADP-ribose) polymerase (PARP) inhibitors, and mammalian target of rapamycin (mTOR) inhibitors, whereas the low-risk group was more sensitive to lapatinib. CONCLUSIONS: The CFH- and SPINT2-based prognostic signature may serve as an independent biomarker for postoperative risk stratification in CCA. High-risk patients, characterized by fibroblast-derived CFH enrichment and malignant-cell SPINT2 loss, exhibit an immunosuppressive microenvironment and may be more suitable for gemcitabine-based chemotherapy or PARP/mTOR inhibitors, whereas low-risk patients may benefit from less intensive adjuvant strategies or HER2/EGFR-targeted lapatinib. Prospective validation is warranted before clinical implementation.

Cholangiocarcinoma (CCA)↗

PubMed2024

Inhibiting the expression of spindle appendix cooled coil protein 1 can suppress tumor cell growth and metastasis and is associated with cancer immune cells in esophageal squamous cell carcinoma.

Inhibiting the expression of spindle appendix cooled coil protein 1 (SPDL1) can slow down disease progression and is related to poor prognosis in patients with esophageal cancer. However, the specific roles and molecular mechanisms of SPDL1 in esophageal squamous cell carcinoma (ESCC) have not been explored yet. The current study aimed to investigate the expression levels of SPDL1 in ESCC via transcriptome analysis using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Moreover, the biological roles, molecular mechanisms, and protein networks involved in SPDL1 were identified using machine learning and bioinformatics. The cell counting kit-8 assay, EdU staining, and transwell assay were used to investigate the effects of inhibiting SPDL1 expression on ESCC cell proliferation, migration, and invasion. Finally, the correlation between the SPDL1 expression and cancer immune infiltrating cells was evaluated by analyzing data from the TCGA database. Results showed that SPDL1 was overexpressed in the ESCC tissues. The SPDL1 expression was related to age in patients with ESCC. The SPDL1 co-expressed genes included those involved in cell division, cell cycle, DNA repair and replication, cell aging, and other processes. The high-risk scores of SPDL1-related long non-coding RNAs were significantly correlated with overall survival and cancer progression in patients with ESCC (P < 0.05). Inhibiting the SPDL1 expression was effective in suppressing the proliferation, migration, and invasion of ESCC TE-1 cells (P < 0.05). The overexpression of SPDL1 was positively correlated with the levels of Th2 and T-helper cells, and was negatively correlated with the levels of plasmacytoid dendritic cells and mast cells. In conclusion, SPDL1 was overexpressed in ESCC and was associated with immune cells. Further, inhibiting the SPDL1 expression could effectively slow down cancer cell growth and migration. SPDL1 is a promising biomarker for treating patients with ESCC.

Humans↗

PubMed2026

Stage-Independent Real-Time Subtype Classification and Comprehensive Biopsy Profiling of Urothelial Carcinomas by the Lund Taxonomy System.

Bladder cancer is a heterogeneous malignancy with diverse clinical outcomes, and conventional pathological assessment alone is insufficient to capture its underlying biology. Gene expression profiling can stratify tumors into molecular subtypes with prognostic and predictive potential, but the reliability of transcriptomic classification and its clinical utility remains to be established. The translational/observational UROSCANSEQ study (ISRCTN15459149) prospectively evaluates RNA-based Lund Taxonomy (LundTax) molecular subtype classification in a clinical setting. Among 784 consecutive biopsies collected between 2018 and 2022, RNA sequencing was successful for 90% of all biopsies, encompassing 662 bladder cancer patients with a stage distribution of 48% Ta, 27% T1, 24% ≥T2, and 1% CIS. We demonstrate that the LundTax subtype classification algorithm, applied to individual samples, accurately identifies cancer cell phenotypes with characteristic gene and protein expression patterns in a manner robust to RNA quality, data preprocessing strategies, and batch effects, supporting its clinical feasibility across both non-muscle-invasive and muscle-invasive disease. We further extend the LundTax framework by incorporating single-sample molecular risk scores reflecting tumor grade, proliferation, and progression risk, as well as tumor microenvironment signatures. Both risk scores and overall immune and stromal content in biopsies were significantly associated with an increased risk of clinical progression in noninvasive disease. In a separate analysis of the relative cellular composition of the tumor microenvironment, however, only the fraction of natural killer cells remained significant. Together, the expanded LundTax system provides a comprehensive molecular portrait of individual tumor biopsies. By explicitly separating cancer cell-intrinsic phenotypes, prognostic indexes, and microenvironmental signals, the framework minimizes biological confounding and establishes a strong foundation for future studies evaluating clinical outcomes and treatment responses.

Humans↗

PubMed2026

Kynurenine metabolism-related gene signature for prognostic stratification in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a major global health burden with high mortality rates and limited therapeutic options. The identification of reliable biomarkers for early diagnosis and prognosis prediction is urgently needed. Kynurenine metabolism, a critical pathway in immune regulation and tumor progression, has been implicated in various cancers. However, its prognostic value in HCC has not been fully elucidated. This study aimed to develop a prognostic risk model based on kynurenine metabolism-related genes (KMRGs) for HCC patients. METHODS: Transcriptomic and clinical data of HCC patients were retrieved from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Survival analysis and functional enrichment analysis were conducted to validate the predictive performance of the model and to investigate the underlying mechanisms. ALDH8A1 was ultimately identified as a target gene based on survival analysis, and its impact on tumor cell migration was assessed using the HCC cell line. RESULTS: A prognostic model based on seven KMRGs was established. The high-risk group exhibited significantly worse overall survival compared to the low-risk group. Functional enrichment analysis in high-risk patients highlighted significant enrichment in core biological processes, including spliceosome assembly and ribonucleoprotein complex biogenesis. Furthermore, a nomogram integrating the risk score and clinical pathological features was developed, demonstrating moderate predictive performance for HCC prognosis. CONCLUSIONS: This study successfully constructed a prognostic risk model based on seven KMRGs, providing a valuable tool for predicting clinical outcomes in HCC patients. These findings highlight the potential role of kynurenine metabolism in HCC progression and offer new insights for future therapeutic strategies.

ALDH8A1↗

PubMed2026

Machine learning and multi-omics clustering to map cellular rewiring and immune evasion in ccRCC.

Immune checkpoint blockade (ICB) efficacy in clear cell renal cell carcinoma (ccRCC) is limited by tumor microenvironment (TME) heterogeneity. Because traditional bulk-derived models lack spatial resolution, we developed an integrated framework connecting macroscopic survival risks to microscopic TME structures. We applied ten algorithms to establish multi-omics subtypes and evaluated 101 machine-learning combinations across three independent cohorts to generate a Consensus Machine Learning-driven Signature (CMLS). The signature's spatial and cellular origins were decoded using spatial transcriptomics (ST) and a 140,000-cell scRNA-seq atlas. Expression of key genes was experimentally validated via RT-qPCR in 17 paired ccRCC clinical tissues. We identified two molecular subtypes with distinct clinical and epigenetic profiles. SuperPC optimization yielded a 24-gene CMLS serving as an independent prognostic factor. scRNA-seq and ST deconvolution revealed these signals predominantly originate from cancer-associated fibroblasts (CAFs) and malignant epithelial cells, which collaborate to drive spatial immune exclusion. RT-qPCR confirmed significant overexpression of five core CMLS genes in ccRCC versus adjacent normal tissues. Low CMLS scores correlated with enhanced ICB responsiveness, whereas high-CMLS tumors demonstrated specific vulnerability to dasatinib and dabrafenib. The CMLS translates spatial immune-exclusion dynamics into a quantifiable metric, outperforming tumor mutational burden in predicting ICB benefits, providing a robust tool for patient stratification in ccRCC.

Humans↗

PubMed2026

Development of m6A-related prognostic models for survival in lung squamous cell carcinoma with different PD-L1 expression levels.

BACKGROUND: Programmed death-ligand 1 (PD-L1) is widely used in the clinical context of immune checkpoint inhibitor therapy, but its relationship with N6-methyladenosine (m6A) RNA methylation in lung squamous cell carcinoma (LUSC) has not been well defined. This study aimed to investigate the association between PD-L1 messenger RNA (mRNA) expression and m6A regulator expression patterns and to develop exploratory m6A-based prognostic models in LUSC. METHODS: Transcriptome data from 502 patients with LUSC were obtained from The Cancer Genome Atlas (TCGA). Patients were divided into PD-L1 high-expression (PHE) and PD-L1 low-expression (PLE) groups according to the median PD-L1 mRNA level. Differential expression and correlation analyses were performed for 30 m6A regulators. Transcriptome sequencing data from surgical specimens from 28 Asian patients with LUSC were used for expression-pattern comparison. Principal component analysis (PCA), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct prognostic models in the TCGA cohort. RESULTS: In the TCGA cohort, the main differentially expressed m6A regulators between the two PD-L1 groups were YTHDF2 (P<0.001), IGF2BP3 (P<0.001), and YTHDC2 (P<0.001). In the Asian cohort, ALKBH5 (P=0.008) and ZC3H13 (P=0.03) showed significant differences. LASSO-Cox models were constructed for the overall LUSC cohort and for the PHE and PLE subgroups. The overall model included METTL3, HNRNPC, and CBLL1, with a 5-year time-dependent area under the receiver operating characteristic curve (AUC) of 0.579. The 5-year AUCs were 0.742 in the PHE subgroup and 0.652 in the PLE subgroup. The risk score remained independently associated with prognosis in multivariate Cox analysis. CONCLUSIONS: In LUSC, PD-L1 mRNA status was associated with distinct m6A regulator expression profiles. In the TCGA cohort, the PHE subgroup showed higher expression of CBLL1, G3BP1, IGF2BP3, FMR1, and YTHDC2, but lower expression of VIRMA, YTHDF2, and PRRC2A compared with the PLE subgroup. In the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CICAMS) cohort, ALKBH5 and ZC3H13 were more highly expressed in the PHE subgroup. Moreover, m6A-based risk models were associated with survival outcomes, with significant prognostic separation in the overall TCGA cohort and the PHE subgroup, whereas the PLE subgroup showed a weaker survival separation.

Lung squamous cell carcinoma (LUSC)↗

PubMed2026

A cuproptosis-related lncRNAs-based risk signature for predicting prognosis and immune status in glioma.

BACKGROUND: Glioma is one of the most prevalent primary malignant brain tumors, characterized by poor prognosis and limited treatment options. Recent studies have identified cuproptosis, a novel copper-dependent form of regulated cell death, as a critical mechanism involved in tumor progression. However, the role of cuproptosis-related long non-coding RNAs (lncRNAs) in glioma remains not fully clarified. This study aimed to develop and validate a prognostic model based on cuproptosis-associated lncRNAs to predict patient outcomes and guide individualizing therapeutic strategies. METHODS: Transcriptomic profiles and clinical data were obtained from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), and the Chinese Glioma Genome Atlas (CGGA) databases. Cuproptosis -related prognostic lncRNAs were filtered via univariate and multivariate Cox and Least absolute shrinkage and selection operator (LASSO) regression analyses, which were selected to establish a prognostic model for glioma. Samples were divided into high- and low-risk groups, and the predictive performance of the prognostic model was evaluated based on receiver operating characteristic (ROC) curves, Kaplan-Meier (K-M) survival curves, and a nomogram. In addition, immune cell infiltration, tumor mutational burden (TMB), immunophenoscore (IPS), Tumor Immune Dysfunction and Exclusion (TIDE) and drug sensitivity were analyzed. Expression levels of selected lncRNAs and proteins were validated using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting. RESULTS: An 11-lncRNA signature associated with cuproptosis was established, and the risk score derived from this model was identified as an independent prognostic factor for glioma. The model exhibited excellent predictive ability, with area under the curve (AUC) values of 0.880, 0.913, and 0.866 for 1-, 3-, and 5-year survival, respectively. Higher TMB, immune checkpoint expression, and IPS were observed in the high-risk group and no significant difference was observed in TIDE between risk groups. Drug sensitivity analysis identified TPCA-1, KIN001-135, and ispinesib mesylate as potential therapeutic agents. Expression validation in glioma cells further supported the biological relevance of the selected lncRNAs. CONCLUSIONS: This cuproptosis-related lncRNA-based signature demonstrates strong prognostic value and may serve as a promising tool for glioma risk stratification and personalized treatment selection.

Glioma↗