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PubMed2024

Post-transcriptional control drives Aurora kinase A expression in human cancers.

Aurora kinase A (AURKA) is a major regulator of the cell cycle. A prominent association exists between high expression of AURKA and cancer, and impairment of AURKA levels can trigger its oncogenic activity. In order to explore the contribution of post-transcriptional regulation to AURKA expression in different cancers, we carried out a meta-analysis of -omics data of 18 cancer types from The Cancer Genome Atlas (TCGA). Our study confirmed a general trend for increased AURKA mRNA in cancer compared to normal tissues and revealed that AURKA expression is highly dependent on post-transcriptional control in several cancers. Correlation and clustering analyses of AURKA mRNA and protein expression, and expression of AURKA-targeting hsa-let-7a miRNA, unveiled that hsa-let-7a is likely involved to varying extents in controlling AURKA expression in cancers. We then measured differences in the short/long ratio (SLR) of the two alternative cleavage and polyadenylation (APA) isoforms of AURKA mRNA across cancers compared to the respective healthy counterparts. We suggest that the interplay between APA and hsa-let-7a targeting of AURKA mRNA may influence AURKA expression in some cancers. hsa-let-7a and APA may also independently contribute to altered AURKA levels. Therefore, we argue that AURKA mRNA and protein expression are often discordant in cancer as a result of dynamic post-transcriptional regulation.

Humans↗

PubMed2026

Targeting the Fatty Acid Binding Protein 5-Specificity Protein 1 Axis Restores Enzalutamide Sensitivity by Suppressing Androgen Receptor/Androgen Receptor Splice Variant 7 Signaling: Implications for Prostate Cancer Therapy.

BACKGROUND: Castration-resistant prostate cancer (CRPC) remains a major clinical challenge driven by persistent androgen receptor (AR) signaling and constitutively active splice variants such as androgen receptor splice variant 7 (AR-V7), which confer resistance to therapies including enzalutamide. Although metabolic reprogramming contributes to disease progression, the integration of metabolic and transcriptional regulators sustaining therapeutic resistance remains incompletely understood. METHODS: We integrated clinical transcriptomic analysis of The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) cohort with mechanistic and functional validation in 22RV1 CRPC cells to investigate the role of the fatty acid binding protein 5-specificity protein 1 (FABP5-Sp1) regulatory axis. RESULTS: Transcriptomic analysis revealed that FABP5 is significantly upregulated in prostate tumors compared with normal tissue and increases with higher Gleason score. In contrast, AR and Sp1 exhibited heterogeneous expression patterns. Mechanistically, genetic ablation of FABP5 markedly reduced AR-V7 expression and restored sensitivity to enzalutamide, leading to suppression of AR signaling. Conversely, FABP5 overexpression increased Sp1 protein levels. Pharmacological inhibition of Sp1 using mithramycin A resulted in coordinated downregulation of FABP5, AR, and AR-V7, along with suppression of peroxisome proliferator-activated receptor gamma (PPARγ) signaling and downstream vascular endothelial growth factor A (VEGFA) expression. Functionally, Sp1 inhibition significantly reduced anchorage-independent growth and invasion. CONCLUSION: These findings define a FABP5-Sp1-AR/AR-V7 transcriptional-metabolic axis driving enzalutamide resistance in CRPC. Targeting FABP5 restores therapeutic sensitivity and represents a promising biomarker and therapeutic strategy in advanced prostate cancer.

AR-V7↗

PubMed2026

Protective TGFβ2/SMAD3 axis identified by TWAS in papillary thyroid cancer.

Papillary thyroid carcinoma (PTC) is the most common endocrine malignancy. Although generally indolent, a subset shows aggressive behaviour. Furthermore, the genetic heterogeneity of PTC is not fully explained by known driver mutations, underscoring the need to identify additional susceptibility genes and regulatory mechanisms. To identify additional susceptibility genes and regulatory mechanisms, we integrated transcriptome-wide association studies (TWAS) with summary-data-based Mendelian randomisation (SMR), joint/conditional testing (JCT), and colocalisation analyses across multiple independent cohorts, followed by heterogeneity in dependent instruments (HEIDI) test. Gene prioritisation analyses consistently highlighted TGFB2 and SMAD3 as candidate susceptibility genes for PTC, with SMR supporting putative protective effects. Besides, GEPIA confirmed the positive correlation between TGFB2 and SMAD3 expression in TCGA-THCA. Functional experiments in TPC-1 cells showed that TGFβ2 treatment inhibited cell proliferation and migration, induced apoptosis, and resulted in G0/G1 cell-cycle arrest, accompanied by increased SMAD3 phosphorylation, suggesting activation of canonical TGFβ signalling. Collectively, these findings bridge population-based genetic inference with mechanistic validation and suggest convergent evidence supporting a tumour-suppressive role of the TGFβ2/SMAD3 axis in PTC.

Humans↗

PubMed2026

In vivo CRISPR screening links NFKB1 to endocrine resistance in ER+ breast cancer.

Resistance to endocrine therapy (ET) remains a major clinical challenge in the treatment of estrogen receptor-positive (ER+) breast cancer, underscoring the need for novel therapeutic targets. To identify genetic drivers of ET resistance, we conducted an in vivo genome-wide CRISPR-Cas9 screen in MCF7 cells implanted into ovariectomized nude mice under estrogen-deprived conditions. Despite the bottlenecks inherent to in vivo pooled screening, recurrent enrichment analysis identified NFKB1 as a candidate regulator of estrogen-independent tumor progression. Functional studies confirmed that NFKB1 deficiency enhanced tumorigenicity and conferred resistance to tamoxifen and fulvestrant both in vitro and in vivo. Mechanistically, transcriptomic and biochemical analyses revealed that NFKB1 deficiency activated canonical NF-κB signaling, leading to inflammatory gene induction and enhanced ER signaling. Furthermore, pharmacologic inhibition of NF-κB signaling restored ET sensitivity in NFKB1-deficient cells. Analysis of TCGA breast cancer datasets revealed reduced NFKB1 expression in luminal breast tumors, whereas expression of other NF-κB family members was largely unchanged. Further analysis showed that low NFKB1 expression was associated with poorer clinical outcomes in patients with ER+ breast cancer. Collectively, these findings identify NFKB1 as a negative regulator of NF-κB signaling and endocrine resistance in ER+ breast cancer and provide mechanistic evidence linking NF-κB activation to ligand-independent ER signaling. Our results support further investigation of NFKB1 as a candidate biomarker and of NF-κB pathway inhibition as a potential therapeutic strategy in endocrine therapy-resistant breast cancer. These findings also illustrate the utility of in vivo CRISPR screening for identifying candidate regulators of endocrine resistance in breast cancer.

ER+ breast cancer↗

PubMed2026

Convergent IGF2 overexpression in pheochromocytoma/paraganglioma: insights from Beckwith-Wiedemann syndrome.

Beckwith-Wiedemann syndrome (BWS) is an imprinting disorder characterized by overgrowth and tumor predisposition, caused by dysregulated expression of genes on chromosome 11p15.5. An association between BWS and pheochromocytoma/paraganglioma (PPGL) has been suggested in isolated case reports over the past fifty years, but the molecular basis for this link remains unclear. We identified four patients with BWS who developed metastatic PPGL and investigated IGF2 pathway activation in these tumors and in PPGL across various genotypes. Pan-cancer transcriptomic analysis of The Cancer Genome Atlas (TCGA) demonstrated that PPGL overexpresses IGF2, with pseudohypoxic tumors exhibiting higher expression compared to other molecular clusters. Loss of heterozygosity and loss of imprinting at 11p15.5 partially explain this overexpression, with PPGL additionally demonstrating globally elevated expression of imprinted genes compared to most other tumor types, suggesting a broader relaxation of genomic imprinting. Cognate receptor profiling revealed that PPGLs are equipped to respond to IGF2 signaling, with high expression of IGF1R and insulin receptor isoform A (IR-A). Immunohistochemistry confirmed IGF2 protein overexpression in both BWS-associated and genotypically diverse sporadic PPGLs. Our results indicate that IGF2 overexpression is a convergent molecular feature of PPGL across genotypes and suggest the IGF2 pathway as a potential diagnostic and therapeutic target.

Humans↗

PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗

PubMed2026

Non-structural maintenance of chromosome condensin I complex subunit H knockdown suppresses malignant progression of esophageal squamous cell carcinoma via the Wnt/β-catenin signaling pathway.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) remains a major cause of cancer-related mortality, and effective therapeutic targets are still limited. Non-structural maintenance of chromosome condensin I complex subunit H (NCAPH) has been implicated in tumorigenesis; however, its clinical relevance, functional roles, and underlying mechanisms in ESCC are not fully defined. We aimed to characterize the expression pattern, prognostic value, biological functions, and mechanistic basis of NCAPH in ESCC. METHODS: Public datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to evaluate NCAPH expression and clinical associations. Single-cell RNA sequencing (scRNA-seq) data were used to map cell-type-specific distribution of NCAPH in tumor and adjacent tissues. NCAPH was silenced in KYSE150 and KYSE510 cells using lentiviral short hairpin RNAs (shRNAs), followed by Cell Counting Kit-8 (CCK-8), colony formation, wound-healing, and Transwell migration/invasion assays. A nude mouse xenograft model was established to assess the effect of NCAPH knockdown in vivo. RNA sequencing (RNA-seq), quantitative polymerase chain reaction (qPCR), western blotting, and enzyme-linked immunosorbent assay (ELISA) were performed to explore potential mechanisms. RESULTS: NCAPH was consistently upregulated in ESCC across multiple cohorts and was associated with unfavorable clinicopathological features and poorer survival. Functional assays demonstrated that NCAPH knockdown significantly inhibited ESCC cell proliferation, migration, invasion, and clonogenic growth. In vivo, NCAPH silencing suppressed xenograft tumor growth. Mechanistically, transcriptomic profiling and molecular validation indicated attenuation of Wnt/β-catenin signaling following NCAPH depletion, accompanied by reduced β-catenin and downstream targets. CONCLUSIONS: NCAPH promotes malignant progression of ESCC, at least in part through activation of the Wnt/β-catenin pathway, and may serve as a potential biomarker and therapeutic target.

Esophageal squamous cell carcinoma (ESCC)↗

PubMed2026

Prognostic value of genes associated with metastasis and propionate metabolism in rectal cancer.

BACKGROUND: Research indicates that alterations in propionate metabolic pathways play a critical role in cancer development and invasion. Postoperative metastatic recurrence remains a major cause of mortality in patients with rectal cancer. However, propionate metabolism-related genes (PMRGs) in rectal cancer remain insufficiently characterized. Therefore, this study aimed to identify prognostic biomarkers associated with lymph node metastasis and propionate metabolism and construct a risk‑prediction model for rectal cancer via bioinformatic analyses. METHODS: The Cancer Genome Atlas-Rectum Adenocarcinoma (TCGA-READ) and GSE87211 datasets, together with a curated PMRGs gene set, were used in this study. Pearson correlation analysis was performed to assess associations between overlapping genes (differentially expressed genes between READ and normal tissues, as well as between N0 and N1-N2 stages) and PMRGs, leading to the identification of candidate genes. Functional enrichment analyses were subsequently conducted to characterize the biological roles of these candidates. Prognostic biomarkers were identified using univariate Cox regression combined with least absolute shrinkage and selection operator (LASSO) regression, and a prognostic model was constructed accordingly. Independent prognostic validation was then performed. In addition, immune checkpoint profiling and immunotherapy response analyses were conducted across risk subgroups. Single-gene Gene Set Enrichment Analysis (GSEA) was applied to elucidate the pathways associated with the identified biomarkers. Finally, drug sensitivity analyses were performed. RESULTS: A total of 157 candidate genes were identified through the analytical pipeline. Functional enrichment analysis indicated that these genes were primarily involved in inflammatory response regulation and tumor necrosis factor (TNF) signaling pathways. Five prognostic biomarkers were subsequently identified and incorporated into a predictive model. External validation using the GSE87211 cohort confirmed the robustness of the model. Risk score and disease status were identified as independent prognostic factors. Six immune checkpoint molecules exhibited differential expression between risk groups. Correlation analyses revealed that the risk score was positively associated with most immune checkpoint genes. Single-gene GSEA demonstrated that the biomarkers were mainly enriched in ribosomal biogenesis and cell adhesion molecule-related pathways. Furthermore, 51 therapeutic agents exhibited significantly different half-maximal inhibitory concentration (IC50) values between risk subgroups. CONCLUSIONS: This study identified five biomarkers (CCL24, IGFBP3, ODC1, PYGM, and VKORC1) associated with lymph node metastasis and propionate metabolism pathways, providing a potential foundation for prognostic prediction in patients with rectal cancer.

Rectal cancer↗

PubMed2026

Screening of molecular biomarkers ASPN and LBH and construction of a prediction nomogram for the progression of esophagogastric junction adenocarcinoma.

BACKGROUND: Esophagogastric junction adenocarcinoma (EGJA) is an aggressive malignancy of the digestive system with poor prognosis. Early diagnosis and accurate prediction of tumor progression remain major clinical challenges. This study aimed to identify and validate molecular biomarkers and construct a precise diagnostic model, providing a scientific basis for individualized treatment. METHODS: Differentially expressed genes (DEGs) associated with EGJA were identified using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was then performed for further screening. The protein expression levels of ASPN and LBH were validated by immunohistochemistry in both tumor and adjacent non-tumor tissues. A nomogram was constructed by integrating clinical and pathological features, and its performance and clinical utility were assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS: Immunohistochemistry demonstrated that the protein expression of ASPN was significantly upregulated in tumor tissues, with expression levels increasing with tumor stage. Conversely, LBH was downregulated in tumor tissues and decreased with advancing stages. The predictive model achieved an area under the curve (AUC) value of 0.977, indicating excellent diagnostic and prognostic performance. DCA confirmed the clinical net benefit of the model. CONCLUSIONS: ASPN and LBH are critical molecular biomarkers for EGJA. The nomogram combining these two markers enables accurate distinction between early and advanced-stage tumors, offering significant support for early diagnosis of EGJA.

ASPN↗

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

MS4A3 as a potential prognostic biomarker for colon cancer: integrated analysis of expression patterns and immune cell infiltration.

BACKGROUND: Membrane Spanning 4-Domains A3 (MS4A3) has been confirmed to possess significant tumor-suppressive potential in various malignancies. However, its expression characteristics and clinical prognostic value in colon cancer (CC) still lack systematic and in-depth investigation. This study aimed to systematically investigate the expression pattern, prognostic value, immune microenvironment association, and biological function of MS4A3 in CC through integrated bioinformatics analyses and experimental validation. METHODS: This study utilized The Cancer Genome Atlas-Colon Adenocarcinoma (TCGA-COAD) cohort to screen for genes significantly associated with CC and combined multiple independent Gene Expression Omnibus (GEO) datasets to validate the expression patterns and prognostic significance of MS4A3. Key biological pathways were identified through gene set enrichment analysis (GSEA), and tumor immune infiltration characteristics were evaluated using the CIBERSORT algorithm. Additionally, the expression of MS4A3 and its impacts on cellular functions were validated at the cellular level through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, Cell Counting Kit-8 (CCK-8), EdU, Transwell, and TUNEL assays. RESULTS: Analysis of public datasets revealed that MS4A3 is significantly downregulated in CC tissues, and its low expression is an independent risk factor for shortened overall survival (OS). GSEA indicated that MS4A3 downregulation is closely associated with the aberrant activation of the pentose phosphate pathway. Immune infiltration analysis showed that low MS4A3 expression is closely linked to the enrichment of M2 macrophages and neutrophils, as well as the upregulation of multiple immune checkpoint genes. In vitro experiments further confirmed that MS4A3 was lowly expressed in CC cell lines. Its overexpression significantly inhibited CC cell viability, proliferation, migration, and invasion, while simultaneously promoting cell apoptosis. CONCLUSIONS: MS4A3 expression is significantly decreased in CC tissues and is significantly correlated with poor prognosis, suggesting that this gene may serve as a potential prognostic biomarker.

MS4A3↗

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

Development and preliminary validation of plasma cell-free DNA methylation-based diagnostic prediction model for colorectal cancer detection.

BACKGROUND: Colorectal cancer (CRC) is a common malignancy associated with genetic and epigenetic alterations. Several methylation biomarkers have been investigated for non-invasive CRC detection; however, their reported performance varies across clinical settings, and the detection of early-stage or precancerous disease and discrimination from non-malignant colorectal conditions remain challenging. This exploratory study aimed to identify reproducible CRC-associated plasma cell-free DNA (cfDNA) methylation regions and to develop and preliminarily evaluate diagnostic prediction model for distinguishing CRC from healthy controls and benign samples. METHODS: Public CRC tissue methylation datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify reproducible CRC-associated methylation alterations. Plasma cfDNA methylation was profiled using methyl-CpG-binding-domain enrichment followed by paired-end sequencing in patients with CRC, patients with colorectal polyps, and healthy controls. After quality-control filtering, 30 CRC and healthy-control samples were randomly allocated at the participant level in a 7:3 ratio to a development set comprising 10 patients with CRC and 11 healthy controls and a held-out test set comprising 4 patients with CRC and 5 healthy controls. Hypermethylated regions were selected using least absolute shrinkage and selection operator (LASSO) logistic regression. The 12-region model was evaluated in the held-out test set and subsequently applied to 10 colorectal polyp samples without refitting or recalibration. RESULTS: Tissue methylation analysis identified reproducible CRC-associated alterations across independent datasets. In the plasma development set, 707 differentially methylated regions (DMRs) were identified between CRC and healthy-control samples, including 324 hypermethylated and 383 hypomethylated regions. LASSO regression selected a 12-region hypermethylation signature. In the held-out test set, the model achieved an area under the curve (AUC) of 0.85 [95% confidence interval (CI): 0.579-1.000]. At the development-set-derived threshold, sensitivity was 75.0% (3/4), specificity was 60.0% (3/5), and accuracy was 66.7% (6/9). When the original model was applied to colorectal polyp samples, model scores were significantly higher in both CRC and polyp samples than in healthy controls, while CRC samples showed a tendency toward higher scores than polyp samples. CONCLUSIONS: This exploratory study identified a 12-region plasma cfDNA hypermethylation signature associated with CRC and developed a LASSO-based diagnostic prediction model that showed preliminary discrimination between CRC and healthy controls in a small held-out test set. By integrating tissue methylation evidence with plasma cfDNA profiling, this study expands the repertoire of candidate region-level methylation markers for blood-based CRC detection.

Colorectal cancer (CRC)↗

PubMed2026

A novel glutamine metabolism-based classification system for characterizing the heterogeneity of hepatocellular carcinoma.

BACKGROUND: Glutamine dependence is a hallmark of tumor cell metabolism, and further molecular classification based on glutamine metabolism in patients with hepatocellular carcinoma (HCC) may provide clinical value. This study thus comprehensively examined the patterns of HCC-specific alterations in glutamine metabolism. METHODS: Consensus clustering analysis was conducted on samples from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset based on glutamine metabolism-related genes, which was validated in the GSE76427, the Liver Cancer-France (LICA-FR) cohort, and the Liver Cancer-Japan (LIRI-JP) cohort from the ICGC. Somatic mutation features were evaluated with the Maftools package in R. The activity of oncogenic pathways was estimated via gene set enrichment analysis (GSEA) or single-sample GSEA (ssGSEA). The tumor microenvironment was analyzed using both the CIBERSORT algorithm (for immune cell infiltration estimation) and the ESTIMATE algorithm (for stromal and immune score calculation). Drug sensitivity and immune checkpoint blockade (ICB) response were also analyzed, for which a classifier was built via least absolute shrinkage and selection operator (LASSO). Immunohistochemistry (IHC) was performed to validate the protein expression levels of key differentially expressed genes (DEGs). Intracellular glutamine content under different glutamine concentrations was measured. The viability of HCC cell lines under varying glutamine concentrations was assessed via Cell Counting Kit-8 (CCK-8) assays. Cell migration and invasion were evaluated through Transwell assays, and protein expression was analyzed via Western blotting. RESULTS: HCC samples were classified into two glutamine metabolism-based clusters, with cluster 1 having a more advanced stage of disease and shorter survival than cluster 2. A higher frequency of genetic mutations and stronger activation of oncogenic pathways was found in cluster 1. There were substantial differences in immune cell infiltration and stromal scores between clusters 1 and 2. Cluster 1 exhibited significantly higher infiltration of immunosuppressive cells and lower stromal scores compared to cluster 2. Cluster 1 had a stronger response to ICB due as indicated by a higher tumor mutation burden (TMB) and T cell-inflamed gene expression profile score, immune checkpoints, and Tumor Immune Dysfunction and Exclusion (TIDE)-predicted data. Moreover, the LASSO classifier accurately differentiated the two clusters. The DEGs between the two clusters were validated in clinical samples. IHC confirmed the differential expression of glutamine metabolism-related genes in HCC samples. CCK-8 assays showed no significant effect of glutamine concentration on cell proliferation. However, Transwell assays revealed that glutamine deprivation (0.2 mM) reduced migration and invasion, while high-glutamine conditions (10 mM) promoted them. Western blotting showed increased expression of metabolism-related proteins under high-glutamine conditions and reduced expression under deprivation. CONCLUSIONS: Altogether, these findings indicate the involvement of glutamine metabolism in HCC and may help inform patient stratification and the formulation of precision therapeutics for this population.

Hepatocellular carcinoma (HCC)↗

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)↗

PubMed2026

Furmonertinib inhibits non-small cell lung cancer progression through ANGPT1-mediated regulation of cell migration and apoptosis.

BACKGROUND: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, highlighting the urgent need for effective therapeutic agents. This study investigates the antitumor effects and underlying mechanisms of furmonertinib (FUR) in lung cancer cells. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to identify FUR target genes, followed by functional enrichment, survival, and protein-protein interaction (PPI) network analyses. Human lung cancer cell line A549 was treated with FUR and/or ANGPT1-specific siRNA. Cell migration, apoptosis, and protein expression were assessed by wound healing, flow cytometry, and Western blotting. Cellular thermal shift assay (CETSA) and drug affinity response target stability (DARTS) assays were used to assess FUR-induced stabilization of angiopoietin-1 (ANGPT1) protein. RESULTS: FUR treatment significantly inhibited cell migration and increased apoptosis in NSCLC cells. Bioinformatics analysis revealed 12 overlapping target genes of FUR from the PharmMapper and SwissTargetPrediction databases, with ANGPT1 emerging as a key candidate. ANGPT1 expression was downregulated in tumor tissues and positively correlated with patient survival. Western blotting confirmed that FUR upregulated ANGPT1 protein levels in a dose-dependent manner. Knockdown of ANGPT1 enhanced migration and suppressed apoptosis, while FUR reversed these effects. FUR treatment enhanced the stability of ANGPT1 under high-temperature conditions while reducing its sensitivity to protease. ANGPT1 may have affected tumor cell migration through cell adhesion and extracellular matrix (ECM) pathways. CONCLUSIONS: FUR suppresses lung cancer progression by upregulating ANGPT1, thereby inhibiting cell migration and promoting apoptosis. ANGPT1 is a potential therapeutic target and provides new insights into the anti-tumor mechanism of FUR in lung cancer.

Lung cancer↗

PubMed2026

Deconvolution of evolutionary architecture unmasks a high-risk, subclonal-rich subtype in treatment-naive small cell lung cancer.

BACKGROUND: Intratumoral heterogeneity (ITH) drives therapeutic resistance in small cell lung cancer (SCLC). However, conventional single-sample analysis has limited horizontal, cross-patient comparisons, leaving the overarching evolutionary architecture in treatment-naive tumors poorly understood. This study aims to deconvolve these architectures to identify clinically relevant evolutionary subtypes. METHODS: We analyzed whole-exome sequencing data from 41 treatment-naive SCLC patients. To overcome the cross-patient comparability bottleneck, we developed a novel probabilistic framework using a refined Gaussian Mixture Model (GMM). This standardized subclonal structures into four hierarchical strata, enabling the identification of evolutionary subtypes via unsupervised clustering. To address the scarcity of SCLC public data, prognostic concordance was robustly explored in The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma (LUSC) based on shared smoking etiology, with lung adenocarcinoma (LUAD) serving as a negative control. RESULTS: The cohort robustly segregated into "Clonal-dominant" (Group 1, n=28) and "Subclonal-rich" (Group 2, n=13) subtypes. Group 1 evolution was primarily driven by tobacco signatures (SBS4). Conversely, Group 2 exhibited late-stage acquisition of a DNA mismatch repair deficiency (MMRd) signature (SBS15), fueling trace subclonal diversification. Clinically, Group 2 demonstrated a significantly lower objective response rate (ORR) to platinum-based regimens (25.0% vs. 81.3%, P=0.02). Furthermore, the Subclonal-rich architecture independently predicted inferior overall survival (OS) [adjusted hazard ratio (adj. HR) =2.93, P=0.02], driven predominantly by limited-stage disease. Cross-cancer analysis validated this histology-dependent, high-heterogeneity adverse pattern in early-stage LUSC but not in LUAD. CONCLUSIONS: This hypothesis-generating study demonstrates that a "Subclonal-rich" architecture, driven by acquired MMRd, identifies high-risk, chemo-resistant SCLC. Our GMM approach suggests that pre-existing heterogeneity may serve as a potential, histology-dependent prognostic marker that warrants prospective validation for tailoring future therapeutic regimens.

Gaussian Mixture Model (GMM)↗

PubMed2026

Pathomics-based machine learning models for predicting METTL5 expression and prognosis in lung adenocarcinoma.

BACKGROUND: METTL5, an N6-methyladenosine (m6A) RNA methyltransferase, has been implicated in tumor progression, but its prognostic value and non-invasive prediction in lung adenocarcinoma (LUAD) remain unclear. This study aimed to develop a pathomics-based machine learning model to predict METTL5 expression from histopathological images and evaluate its prognostic significance in LUAD. METHODS: A total of 327 LUAD patients from The Cancer Genome Atlas (TCGA) with matched hematoxylin and eosin (H&E) slides, transcriptomic, and clinical data were included and randomly divided into training and validation sets (7:3). Quantitative histopathological features were extracted using PyRadiomics. Feature selection was performed via maximum relevance minimum redundancy (mRMR) and recursive feature elimination (RFE), followed by construction of a Gradient Boosting Machine (GBM) model. A pathomics score (PS) was generated to assess prognostic relevance. Survival analyses, gene set variation analysis (GSVA), tumor mutational burden (TMB), immune infiltration analysis, and in vitro functional assays were conducted. RESULTS: METTL5 overexpression was independently associated with poor overall survival [hazard ratio (HR) =1.637, P=0.007]. The model achieved good predictive performance [area under the curve (AUC) =0.847 in the training set and 0.752 in the validation set]. High PS was significantly associated with worse survival and remained an independent prognostic factor (HR =1.563, P=0.03). Elevated PS correlated with altered metabolic pathways, increased TMB, and immune microenvironment changes. METTL5 knockdown reduced proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) in A549 cells. CONCLUSIONS: The pathomics-based model accurately predicts METTL5 expression and provides prognostic stratification in LUAD, supporting its potential as a practical imaging-derived biomarker.

Methyltransferase-like 5↗