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PubMed2026

Integrative analysis identifies a glycosylation-related lncRNA signature associated with prognosis in kidney renal clear cell carcinoma.

BACKGROUND: Glycosylation and long non-coding RNAs (lncRNAs) play critical roles in tumor progression. However, the prognostic significance of glycosylation-related lncRNAs (GRLncs) in kidney renal clear cell carcinoma (KIRC) remains largely unclear. This study aimed to identify prognostic GRLncs and construct a predictive model for KIRC prognosis. METHODS: Transcriptomic and clinical data of KIRC patients were analyzed to identify GRLncs associated with overall survival (OS). A prognostic model was constructed based on selected GRLncs, and its predictive performance was evaluated using Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) curves, and univariate and multivariate Cox regression analyses. Patients were stratified into high- and low-risk groups according to the median risk score, and internal validation was performed using training and testing cohorts to assess the stability of the model. Tumor microenvironment characteristics, immune checkpoint expression, immunotherapy response, and drug sensitivity were further analyzed. In addition, the expression of three signature lncRNAs was validated by real-time quantitative polymerase chain reaction (RT-qPCR) in 10 paired KIRC tumor and adjacent normal tissues. Functional roles of selected lncRNAs were investigated using antisense oligonucleotides (ASOs)-mediated knockdown in KIRC cell lines, followed by Cell Counting Kit 8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU) incorporation, colony formation, and migration assays. RESULTS: Five GRLncs (AC093278.2, EPB41L4A-DT, DLGAP1-AS2, AC084876.1, and AC005261.3) were identified and used to construct a prognostic model. AC093278.2 and EPB41L4A-DT were protective factors, whereas DLGAP1-AS2, AC084876.1, and AC005261.3 were risk factors. KM analysis on GRLncs-based risk score stratification revealed patients in the high-risk group had significantly poorer OS than those in the low-risk group. ROC analysis and Cox regression demonstrated that the GRLnc-based risk score served as an independent predictor of KIRC prognosis and exhibited favorable predictive performance compared with conventional clinical variables. High- and low-risk groups also exhibited distinct immune microenvironment characteristics, immune checkpoint expression patterns, and predicted drug sensitivities. RT-qPCR detected significant downregulation of protective factor-EPB41L4A-DT in KIRC tissues, while risk factors-DLGAP1-AS2 and AC084876.1 showed expression trends consistent with their predicted risk attributes. Functional experiments further revealed that knockdown of DLGAP1-AS2 and AC084876.1 suppressed proliferation and migration of KIRC cells, whereas knockdown of EPB41L4A-DT promoted these processes, supporting the biological relevance of these three signature lncRNAs. CONCLUSIONS: This study establishes a novel prognostic model based on five GRLncs that showed promising performance in The Cancer Genome Atlas (TCGA)-based analyses of KIRC. The combined clinical expression analysis and functional validation of three constituent GRLncs (DLGAP1-AS2, EPB41L4A-DT, and AC084876.1) supports the biological plausibility of the model and suggest that GRLncs may serve as potential prognostic biomarkers and therapeutic targets for KIRC.

Kidney renal clear cell carcinoma (KIRC)↗
PubMed2026

Integrated transcriptome analysis and machine learning to construct a homeostatic model of acetylation for bladder cancer and validate the key gene CES1.

BACKGROUND: Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system. Protein acetylation (PA) plays a critical role in regulating multiple biological processes (BPs), cellular homeostasis, and cancer-related signaling pathways. This study aimed to construct a homeostatic model of acetylation for BLCA using integrated transcriptome analysis and machine learning and to validate the key gene CES1. METHODS: RNA sequencing (RNA-seq) and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Acetylation-related differentially expressed genes (DEGs) in BLCA were screened using differential expression analysis (DEA). An acetylation homeostatic model was constructed via univariate, machine learning-based least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, followed by validation in multiple cohorts. Single-cell RNA-seq analysis was used to explore gene expression patterns in diverse cell types. Enrichment analysis (EA), immune infiltration, and drug sensitivity analysis (DSA) were performed to characterize molecular features of different risk groups. Finally, the biological function of CES1 as the key gene was verified by in vitro knockdown experiments. RESULTS: We established a robust acetylation homeostatic model consisting of five genes, which effectively predicted overall survival (OS) and served as an independent prognostic factor in BLCA. High-risk patients showed significantly poorer prognosis, distinct immune infiltration profiles, and differential drug sensitivity. CES1 was identified and validated as the key gene in this model, which was highly expressed in BLCA and associated with poor prognosis. Knockdown of CES1 markedly suppressed cell proliferation, invasion, and migration, and reduced intracellular coenzyme A (CoA) levels, thereby regulating PA homeostasis. CONCLUSIONS: We developed and validated a novel acetylation homeostatic model for survival stratification and personalized treatment guidance in BLCA, based on integrated transcriptome analysis and machine learning. CES1 is closely associated with intracellular CoA levels and the malignant progression of BLCA. Its potential association with PA homeostasis requires further mechanistic validation, and it may act as a candidate therapeutic biomarker for BLCA.

Bladder cancer (BLCA)↗
PubMed2026

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗
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↗
PubMed2026

RECQL correlates with immune infiltration and serves as a prognostic biomarker and therapeutic predictor in gastric cancer.

BACKGROUND: RecQ-like helicase (RECQL), a member of the RecQ-like DNA helicase family, plays a crucial role in maintaining genomic stability. However, its relevance in gastric cancer (GC) has not been fully investigated. This study aimed to explore the clinical significance, biological functions, and potential role of RECQL in the tumor immune microenvironment of GC through comprehensive bioinformatics analyses and in vitro experiments. METHODS: Weighted gene co-expression network analysis (WGCNA), differential expression analysis, and least absolute shrinkage and selection operator (LASSO) regression were performed using public datasets [The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD), GSE150290] to identify key genes associated with GC progression. Subsequently, key pathways were identified through functional enrichment analysis, while immune infiltration and spatial transcriptomic analyses were conducted to characterize RECQL expression and its association with the tumor immune microenvironment. Finally, the effects of RECQL knockdown on the biological function of GC cells were assessed through Cell Counting Kit-8 (CCK-8), colony formation, scratch, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assays. RESULTS: RECQL was significantly upregulated in GC tissues and correlated with advanced clinical stage and poor prognosis. Gene set enrichment analysis (GSEA) revealed a strong association between high RECQL expression and DNA repair pathway. Immune infiltration analysis indicated significant enrichment of M2 macrophages in the high-RECQL group, along with upregulation of immune checkpoint molecules including PDCD1, CTLA4, and CD274. Spatial transcriptomics further demonstrated co-localization of RECQL with myeloid cell-enriched regions in tumor parenchymal areas. Furthermore, in vitro experimental results indicated that RECQL was highly expressed in GC cell lines, and its knockdown effectively inhibited the viability, proliferation, and migration capabilities of HGC-27 cells, while enhancing their apoptosis. CONCLUSIONS: RECQL serves as a promising biomarker and potential therapeutic target in GC.

DNA repair↗
PubMed2026

Overexpression of TCF7L2 promotes the viability and migration of MHCC-97H human hepatocellular carcinoma cells by upregulating MT-ND4L.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly aggressive cancer with high metabolic adaptability. TCF7L2, a transcription factor implicated in type 2 diabetes and cancer, is overexpressed in HCC. However, its specific role in HCC metabolic reprogramming is not well defined. We aimed to elucidate the previously unrecognized molecular mechanisms through which TCF7L2 impacts HCC progression. METHODS: To investigate the function of TCF7L2, a stable MHCC-97H cell line with TCF7L2 overexpression was established via lentiviral transduction. Cell viability and migration were assessed by Cell Counting Kit-8 (CCK-8) and Transwell assays. Transcriptomic profiling [RNA sequencing (RNA-seq)] was performed to identify differentially expressed genes (DEGs). Functional enrichment analysis [Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA)] and bioinformatics promoter analysis (the JASPAR CORE database) were conducted. Clinical correlations, survival analysis, and tumor microenvironment (TME) interrogation were performed using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort and single-cell datasets [Human Protein Atlas (HPA), CellChat]. Drug sensitivity was predicted via the Genomics of Drug Sensitivity in Cancer (GDSC) database. RESULTS: TCF7L2 overexpression significantly promoted HCC cell proliferation and migration. Transcriptomic analysis revealed that TCF7L2 drives a profound metabolic shift, with key enrichments in lipid homeostasis, fatty acid β-oxidation, and the PI3K/Akt pathway. Mechanistically, TCF7L2 directly binds to the promoter of CPT1A, the rate-limiting enzyme of fatty acid oxidation, and indirectly upregulates the mitochondrial gene MT-ND4Lvia a strong positive correlation with the mitochondrial transcription factor TFAM. In clinical cohorts, TCF7L2 was overexpressed in HCC and its expression correlated positively with MT-ND4L, MKI67, and SNAI1, and served as a predictor of poor overall survival (OS). Furthermore, TCF7L2-high tumors were enriched in hepatic progenitor cell (HPC)-like niches, mediated by enhanced ANGPTL4 signaling. High TCF7L2 expression predicted increased sensitivity to PI3K/mTOR pathway inhibitors. CONCLUSIONS: TCF7L2 acts as a master metabolic regulator in HCC, coordinating lipid catabolism and mitochondrial biogenesis to drive aggressive tumor behavior. It further remodels the TME towards an HPC-like state and predicts sensitivity to metabolic-targeted therapies. These findings identify TCF7L2 as a key prognostic biomarker and a promising therapeutic target.

MHCC-97H hepatocellular carcinoma cells (MHCC-97H ↗
PubMed2026

Mitochondria related gene signature serves as prognosis prediction and risk stratification of cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CHOL) is a highly aggressive biliary malignancy with poor clinical outcomes and limited effective prognostic biomarkers. Mitochondrial dysfunction participates in multiple oncological processes of CHOL, yet the prognostic roles of mitochondria‑related genes (MRGs) remain poorly understood. This study aimed to characterize MRGs expression in CHOL and develop a molecular prognostic model for predicting patient survival and guiding clinical management. METHODS: RNA sequencing (RNA-seq) and clinical data of CHOL were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) (GSE89748) databases. Differentially expressed MRGs were identified, and 10 machine learning algorithms were used to construct prognostic models. The optimal model (highest average C-index) was selected to establish a mitochondria-related risk score (MRRS), which was validated internally and externally. A nomogram integrating clinical factors and MRRS was developed, and biological mechanisms were explored via functional and immune analyses. RESULTS: A 3-MRG (MAP3K1, MRPL18, PYGB) prognostic signature was constructed, stratifying patients into high- and low-risk groups with significantly different overall survival. The model showed high predictive accuracy, with an area under the curve (AUC) up to 0.845, and MRRS was an independent prognostic factor. The signature was associated with mitochondrial pathways, and the high-risk group had distinct immune infiltration and mutation profiles. CONCLUSIONS: A validated MRG prognostic model effectively stratifies CHOL patients and has potential clinical value for prognosis prediction. Further validation in larger cohorts is needed to confirm its applicability.

Cholangiocarcinoma (CHOL)↗
PubMed2026

PSMB8: an immune-related prognostic marker for low-grade gliomas.

BACKGROUND: Glioma is the most common primary intracranial tumor in adults. As a subunit of immune proteasome, proteasome subunit beta type-8 (PSMB8) may regulate the progression of glioma via participating in degradation and presentation of tumor antigenic peptides, but its prognostic and clinical applicant usage is under investigation. Therefore, this study aimed to comprehensively evaluate the prognostic significance of PSMB8 in low-grade glioma (LGG) and to elucidate its association with the tumor immune microenvironment and potential as a predictor for immunotherapy response. METHODS: Transcriptome data were downloaded from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Gene Expression Omnibus (GEO) repositories. The correlations between PSMB8 expression and the clinicopathological features of LGG were investigated in our study, and the prognostic role of PSMB8 in LGGs was assessed fully and comprehensively. Furthermore, we evaluated the correlation between PSMB8 expression and LGG immune environment via the experiments and bioinformatic analysis. RESULTS: Our results indicated that, PSMB8 were highly expressed in most tumor tissues, including LGG. Lower expression of PSMB8 was significantly correlated with lower World Health Organization (WHO) grade and isocitrate dehydrogenase (IDH) mutation status. Moreover, PSMB8 showed a promising prognostic ability for LGG patients via nomogram model and receiver operating characteristic (ROC) curves. Association analysis showed that PSMB8 expression was associated with immune cell infiltration in a variety of tumors, including LGG. Our experiments validated the positive correlation between PSMB8 expression and M2-macrophage infiltration level in clinical LGG tissues and invasive ability of LGG cell. CONCLUSIONS: PSMB8 could be used as one of the prognostic indicators of LGG and it could regulate the LGG cell migratory and invasive ability. Besides, PSMB8 is expected to be a promising biomarker of cancer immunotherapy.

Low-grade glioma (LGG)↗
PubMed2026

Integrative analysis of single-cell sequencing identifies CD8+ TIM3+ CD101+ T cell-associated genes as prognostic biomarkers in breast cancer.

BACKGROUND: Breast cancer is a prevalent and deadly malignancy that significantly impacts women's quality of life and imposes financial burdens. Despite therapeutic advancements, tumour heterogeneity and frequent relapses remain major challenges. Accordingly, this study aimed to characterize immune features associated with CD8+ TIM3+ CD101+ T cells and develop a prognostic signature for breast cancer. METHODS: This study integrated single-cell and bulk transcriptomic datasets to characterize CD8+ TIM3+ CD101+ T cell (CCT)-related immune features and construct a prognostic signature in breast cancer. Single-cell RNA-seq data were sourced from the Gene Expression Omnibus (GEO) repository, and bulk transcriptomic data were from The Cancer Genome Atlas (TCGA) and GEO databases. Analytical methods included pseudo-time trajectory reconstruction (Monocle2), intercellular signalling analysis (CellChat), functional enrichment (ClusterProfiler), and immune profiling (ssGSEA). Prognostic modeling was conducted using least absolute shrinkage and selection operator (LASSO) Cox regression, with validation via Kaplan-Meier and time-dependent receiver operating characteristic (ROC) analyses. RESULTS: Single-cell analysis identified 17 clusters spanning seven cell types, including T cells, myeloid cells, and epithelial cells. T-cell sub-clustering revealed four subtypes. Pseudotime analysis suggested a potential state-transition relationship between CD8+ CD101- TIM3+ and CD8+ CD101+ TIM3+ T-cell states. A total of 121 differentially expressed genes were enriched in vital biological processes. An 11-gene prognostic model showed strong predictive power across cohorts. Single-cell T-cell reclustering identified a CD8+ CD101+ TIM3+ T-cell subpopulation, which was primarily characterized by the expression of markers such as CD101 and HAVCR2/TIM3. CONCLUSIONS: This study maps cellular heterogeneity and molecular networks in breast cancer, offering insights for targeted therapy and improved prognosis.

Breast invasive carcinoma↗
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

Development and internal validation of a six-gene prognostic model based on galactose metabolism for overall survival in lung adenocarcinoma.

BACKGROUND: Lung cancer remains a leading cause of cancer incidence and mortality globally. Metabolic reprogramming promotes tumor progression and shapes an immunosuppressive tumor microenvironment. Galactose metabolism is involved in multiple malignancies, but its prognostic value in lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop and internally validate a galactose metabolism-related multigene prognostic model for LUAD. METHODS: A retrospective prognostic model development and internal validation study was performed using RNA sequencing (RNA-seq) and clinical data from 585 LUAD patients in The Cancer Genome Atlas (TCGA). Differential expression, functional enrichment, univariate and multivariate Cox regression were applied to construct a prognostic gene signature. Internal validation was performed using bootstrap resampling. Model performance was evaluated by time-dependent receiver operating characteristic (ROC), C-index, calibration, and Kaplan-Meier analysis. Associations between the model and immune infiltration, immunotherapy responsiveness, and tumor stemness were also analyzed. RESULTS: A six-gene prognostic model (GALT, GANC, PGM1, GALM, B4GALT1, PGM2) was developed. The model showed good discrimination with 1-, 3-, and 5-year area under the curve (AUC) values of 0.719, 0.693, and 0.684, respectively. The low-risk group exhibited significantly longer survival, increased antitumor immune infiltration (CD8+ T cells, M1 macrophages, activated CD4+ memory T cells), higher expression of T cell proliferation-related genes, lower immune checkpoint expression, better predicted immunotherapy response, and lower tumor stemness compared with the high-risk group. CONCLUSIONS: We developed and internally validated a six-gene prognostic model for LUAD based on galactose metabolism. The model shows moderate prognostic performance and is associated with antitumor immunity and tumor stemness. It may be used for prognostic risk stratification and to guide personalized immunotherapy in LUAD.

Galactose metabolism↗
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

CST2 promotes melanoma malignant phenotypes through the IL-6-STAT3-NF-κB signaling axis.

BACKGROUND: Melanoma is a highly aggressive malignancy with increasing incidence and mortality over recent decades. Cystatin SA (CST2) encodes a secreted cysteine protease inhibitor that is overexpressed in various cancers and promotes tumor progression; however, its role in melanoma remains unclear. This study aimed to investigate the expression and clinical significance of CST2 in melanoma as well as its biological functions and underlying molecular mechanisms in melanoma progression. METHODS: CST2 expression was analyzed in melanoma tissues using The Cancer Genome Atlas (TCGA) dataset. The prognostic value of CST2 was assessed using Kaplan-Meier analysis. In vitro gain- and loss-of-function experiments were performed to evaluate the effect of CST2 on melanoma cell proliferation, colony formation, and migration. Mechanistic studies included protein-protein docking and co-immunoprecipitation to detect the interaction between CST2 and interleukin-6 (IL-6). Western blotting was used to examine the activation status of IL-6 downstream signaling pathways. RESULTS: CST2 was significantly upregulated in melanoma tissues and was correlated with poor patient prognosis. Overexpression of CST2 promoted melanoma cell proliferation, clonogenicity, and migration, whereas CST2 knockdown suppressed these malignant behaviors. CST2 interacted with IL-6, and its knockdown reduced the expression of IL-6, phospho-STAT3, and phospho-NF-κB. These suppressive effects were reversed by IL-6 overexpression, indicating that CST2 exerted its oncogenic effects through the IL-6-STAT3-NF-κB axis. CONCLUSIONS: CST2 promotes melanoma progression by activating the IL-6-STAT3-NF-κB signaling pathway, and may serve as a potential prognostic biomarker and therapeutic target in melanoma.

CST2↗
PubMed2026

Apolipoprotein E promotes papillary thyroid carcinoma progression by activating PINK1/Parkin-mediated mitophagy.

BACKGROUND: Increasing evidence supports a progression-related role of apolipoprotein E (APOE) in papillary thyroid carcinoma (PTC), yet a clear mechanistic explanation for this association is still lacking. Considering the pivotal role of mitochondrial homeostasis in tumorigenesis, the potential role of APOE in promoting PTC progression through mitophagy regulation was investigated. Additionally, the involvement of the PINK1/Parkin-associated pathway in this process was examined to provide insights into its contribution to tumor progression. METHODS: APOE in thyroid carcinoma was characterized in terms of its expression profile, diagnostic relevance, and potential biological functions, based on integrative evidence derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. APOE and mitophagy-related protein expression were further examined in PTC tissues by immunohistochemistry. Further evaluation of APOE in PTC cell lines focused on its association with proliferation, apoptosis, and mitophagy, with bidirectional functional perturbation serving as the basis for assessment. Pharmacological inhibitors were used to assess the involvement of mitophagy-related signaling in the observed APOE-dependent phenotypes. Additionally, the in vivo impact of APOE on PTC tumor growth and mitophagy was further investigated through a nude mouse xenograft model, providing insight into its potential role in tumor progression. RESULTS: A significant upregulation of APOE was observed in thyroid carcinoma tissues and PTC cell lines, supporting its potential relevance as a diagnostic biomarker. The modulation of APOE expression significantly influenced PTC cell proliferation and apoptosis, with overexpression promoting cell proliferation and inhibiting apoptosis, while knockdown led to the opposite effects. Mechanistically, APOE overexpression increased AMP-activated protein kinase (AMPK) phosphorylation and decreased mammalian target of rapamycin (mTOR) phosphorylation, accompanied by increased PINK1 and Parkin expression and mitophagy-related changes, including altered mitochondrial membrane potential, reduced overall reactive oxygen species levels, and increased autophagosome formation. Pharmacological inhibition of mitophagy attenuated the proliferative and antiapoptotic effects of APOE. CONCLUSIONS: These findings demonstrate that APOE promotes PTC progression in association with PINK1/Parkin-related mitophagy and modulation of the AMPK/mTOR axis. The APOE-associated mitophagy axis may provide a rationale for future preclinical investigation in PTC.

Apolipoprotein E (APOE)↗
PubMed2026

Construction and validation of a β-hydroxybutyrylation-related molecular model for predicting prognosis of papillary thyroid carcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) usually has a favorable prognosis, yet a subset of patients develops persistent, recurrent, or biologically aggressive disease. The clinical relevance of lysine β-hydroxybutyrylation (Kbhb)-related transcriptional programs in PTC remains unclear. Accordingly, this study aimed to characterize Kbhb-related molecular heterogeneity in PTC, construct a prognostic signature, and explore its association with the tumor microenvironment (TME). METHODS: Transcriptomic and clinical data from PTC samples within The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) cohort were analyzed to identify Kbhb-related differentially expressed genes (DEGs), define molecular subtypes, construct a prognostic signature, and characterize tumor microenvironmental features. Single-cell RNA-sequencing data from PTC were further used to explore the cellular distribution of representative genes. RESULTS: We identified 51 Kbhb-related DEGs in PTC and defined two Kbhb molecular subtypes. The Kbhb_C2 subtype showed shorter progression-free interval (PFI) and a more immune- and stroma-enriched microenvironment. A six-gene prognostic signature comprising TARID, CDSN, PIMREG, KLRC1, SYT13, and NPR3 was then established. High-risk patients had significantly worse PFI in the full, training, and testing cohorts, with 1-, 3-, and 5-year areas under the curve (AUCs) of 0.715, 0.793, and 0.771, respectively, in the full cohort. High-risk tumors also exhibited higher stromal, immune, and ESTIMATE scores, altered immune infiltration, and increased expression of multiple immune checkpoint molecules. Single-cell analysis confirmed distinct cell-type-specific expression patterns of representative genes. CONCLUSIONS: Kbhb-related transcriptional programs define clinically relevant molecular heterogeneity in PTC and are closely associated with prognosis and TME remodeling. The identified six-gene signature provides a biologically interpretable framework for risk stratification in PTC.

Papillary thyroid carcinoma (PTC)↗
PubMed2026

Pan-cancer characterization of HMGA1 reveals its oncogenic role in tumor microenvironment and stemness: functional validation in pancreatic cancer migration and invasion.

BACKGROUND: HMGA1 is a chromatin-associated oncogenic factor implicated in tumor progression, epithelial-mesenchymal transition (EMT), stemness, and metastasis. However, its pan-cancer expression and prognostic patterns, epigenetic activation, and relationship with malignant-cell stemness/plasticity and tumor microenvironment (TME) remodeling in pancreatic adenocarcinoma (PAAD) remain incompletely defined. This study aimed to systematically characterize HMGA1 across cancers and clarify its clinical and biological relevance in PAAD. METHODS: Pan-cancer transcriptomic, clinical, genetic, methylation, immune, and stemness data were integrated from multiple public databases. PAAD single-cell RNA sequencing data were analyzed to localize HMGA1 expression, infer malignant-cell pseudotime, calculate a stemness module score, and assess ligand-receptor communication using CellChat. Public HMGA1-knockdown RNA sequencing data were reanalyzed to evaluate transcriptional remodeling. The Cancer Genome Atlas (TCGA)-PAAD expression and methylation data were used to assess TME-remodeling, immune-suppression, stemness/plasticity, cytokine/chemokine, checkpoint, and promoter-methylation features. HMGA1 expression and function were further examined using immunohistochemistry (IHC), quantitative real-time polymerase chain reaction, Western blotting, wound-healing assays, and Transwell migration and invasion assays. RESULTS: HMGA1 was upregulated in most tumor types, and high expression was associated with unfavorable survival in multiple cancers, including PAAD. In PAAD, HMGA1 was enriched in malignant epithelial cells and positively correlated with pseudotime (Spearman's rho =0.594), while the stemness module score increased along pseudotime (rho =0.748). HMGA1-high malignant cells showed markedly stronger CellChat-inferred outgoing communication, predominantly involving extracellular matrix (ECM)-receptor, adhesion-related, and selected immunomodulatory ligand-receptor axes. HMGA1 knockdown was associated with broad remodeling of EMT, TGF-β, Hedgehog, IL6/JAK/STAT3, KRAS, and cancer stem cell/stemness-related programs rather than uniform suppression of these programs. HMGA1 promoter methylation was inversely correlated with HMGA1 expression (rho =-0.633) and the TME-remodeling score (rho =-0.347). HMGA1 was associated with selected mediators, including PPIA, PLAU, ANXA1, LGALS9, TGFB1, CD276, and CD47, but not with a generalized checkpoint-high phenotype. Functionally, HMGA1 knockdown significantly reduced pancreatic cancer (PC) cell migration and invasion. CONCLUSIONS: These findings support an association-based model in which promoter hypomethylation-associated HMGA1 activation is linked to malignant epithelial stemness/plasticity, ECM/adhesion-dominant TME remodeling, selected immunomodulatory programs, and aggressive PAAD phenotypes. Further mechanistic and clinical validation is required before HMGA1 can be used for therapeutic stratification or immunotherapy-response prediction.

HMGA1↗
PubMed2026

CEBPG demonstrates oncogene functions and modulates the expression of genes involved in cervical cancer related pathways.

BACKGROUND: Cervical cancer is a major cause of female cancer incidence and mortality globally. Targeted therapy shows promise with higher efficacy and fewer side effects, yet effective biomarkers are needed. CEBPG, a bZIP transcription factor, has attracted attention as a potential biomarker and therapeutic target. It promotes tumor growth in various cancers, but its role in cervical cancer is unclear. The aim of this study is to investigate the expression, biological function, and transcriptional regulatory mechanisms of CEBPG in cervical cancer. METHODS: This study analyzed CEBPG expression in cervical cancer using The Cancer Genome Atlas (TCGA) data, overexpressed it in HeLa cells, and conducted proliferation, apoptosis, migration, and invasion experiments. RNA sequencing (RNA-seq) revealed CEBPG's transcriptional regulatory network. RESULTS: Our findings revealed that CEBPG expression was significantly higher in cervical cancer than in normal tissues. Overexpression of CEBPG in HeLa cells significantly enhanced proliferation, migration, and invasion while repressing apoptosis. RNA-seq analysis identified numerous differentially expressed genes (DEGs) enriched in pathways critical for tumorigenesis and progression, such as angiogenesis, cell proliferation, and migration. Bioinformatics analysis identified 27 potential transcriptional targets of CEBPG, several of which were associated with poor prognosis and higher expression in cervical cancer tissues. CONCLUSIONS: CEBPG may contribute to malignant phenotypes in cervical cancer, potentially through the transcriptional regulation of specific target genes. The high expression of CEBPG in cervical cancer and its demonstrated role in promoting malignancy through transcriptional regulation provide novel insights into the molecular mechanisms of cervical carcinogenesis and progression. This offers directions for further exploration of CEBPG as a potential therapeutic target and biomarker.

CEBPG↗
PubMed2026

Identification of a novel signature for prognostic stratification and integrative analyses in lung adenocarcinoma.

BACKGROUND: Recently, research has revealed that the Golgi apparatus is involved in the development process of cancer; however, the specific effect of Golgi apparatus-related genes (GAGs) in lung adenocarcinoma (LUAD) remains unclear. This study aims to construct a more concise and practical risk model in LUAD using GAG. METHODS: The gene expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and GAGs were downloaded from the Gene Set Enrichment Analysis (GSEA) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were performed to identify the prognostic GAG signature. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. The correlation between the risk model and the immune landscape was examined using CIBERSORT and TIDE analyses. Also, the genes in the signature were assessed by single-cell RNA sequencing (scRNA-seq). RESULTS: A prognostic signature comprising 5 GAG genes (GNPNAT1, RGS20, CAV3, NTSR1, and FURIN) was established after LASSO and multi-Cox analyses. Both the Kaplan-Meier analysis and the ROC curves supported the strong predictive utility of the risk model. Specifically, the former yielded significant stratification in all three validation datasets (P=1.2001e-05, P=0.006, and P=0.04), while the latter provided further evidence of its predictive precision through the area under the curve. In addition, we found that the low-risk group responded better to immunotherapy than the high-risk group (P<0.0001). scRNA-seq analysis revealed the distribution patterns of the 5 GAG genes in cells. Finally, we assessed the situation of tumor mutation burden (TMB) and performed functional analysis based on the risk model of GAGs. CONCLUSIONS: The risk model based on GAGs can effectively stratify the prognosis of patients and predict immunotherapy responses in LUAD.

Golgi apparatus↗