Prognostic significance and therapeutic implications of migrasome-related genes in Sorafenib treatment for primary liver cancer.

Computational multi-omics modelling identifies TOP2A as a central prognostic biomarker and therapeutic target in renal cell carcinoma.

Vestibular schwannoma (VS) is a benign tumor originating from the vestibular nerve, and its complex tumor microenvironment presents significant challenges for research. This study employs single-cell transcriptome sequencing to comprehensively investigate the molecular characteristics of Schwann cells within VS and their potential immunoregulatory roles in the tumor microenvironment. We identified a novel subpopulation of Schwann cells, termed Schwann 4, which shows significantly expressed MHC class II molecules and co-enrichment of PI3K signaling, focal adhesion, and cell adhesion-related pathways, suggesting its potential involvement in tumor-immune modulation. Utilizing machine learning models, we pinpointed 179 genes significantly expressed in Schwann 4, with CTSZ emerging as a candidate gene. CTSZ expression is elevated in VS and is significantly correlated with the PI3K pathway. Comparative analysis revealed that CTSZ promotes proliferation in malignant CNS tumors, whereas its role in benign VS appears distinct. Experimental analysis revealed that CTSZ is correlated with regulatory T cells (Tregs) functionality markers via the PVR-TIGIT pathway, suggesting a potential mechanism for Schwann cell-mediated immune modulation. Functional assays further demonstrated that CTSZ knockdown impairs cell migration and focal adhesion kinase activity. Drug sensitivity analyses identified candidate agents such as YM201636 and AT7867, though their applicability to VS requires dedicated validation. This study contributes to understanding the molecular mechanisms of Schwann cells in benign nerve sheath tumors while providing preliminary evidence for future therapeutic investigation.

PYCR2 contributes to sunitinib resistance in hepatocellular carcinoma by activating the PPAR signaling pathway.

Prognostic value and therapeutic potential of the cuproptosis-related gene LOXL2 in thyroid cancer.

Hepatocellular carcinoma (HCC) treatment faces significant challenges, particularly in tumor growth, metastasis, and drug resistance. While several predictive models exist, effective models that accurately predict patient prognosis and guide targeted therapy decisions remain insufficient. Regulated cell death (RCD) pathways play a pivotal role in the development and progression of various cancers, offering potential prognostic indicators and biomarkers of drug sensitivity for HCC patients. We analyzed multi-cohort transcriptomic data (TCGA, GSE14520, ICGC) and single-cell RNA sequencing data (GSE149614) to identify differentially expressed RCD-related genes (DEGs). A prognostic model, the Regulated Cell Death Index (RCDI), was constructed using machine learning algorithms to stratify HCC patients into high- and low-RCDI groups. Single-cell analysis was employed to examine tumor microenvironment heterogeneity between these groups, and drug sensitivity analysis assessed differences in immune therapy, targeted therapy, and chemotherapy responses based on RCDI subgroups. RCDI was significantly associated with poor clinical features and shorter overall survival, with results validated across all cohorts. Enrichment analysis revealed that high RCDI is correlated with key cancer-related pathways, including the PI3K-Akt pathway and cell cycle regulation. High RCDI was also associated with immune cell infiltration and the expression of immune checkpoint molecules, as validated through single-cell RNA sequencing. Patients with high RCDI exhibited higher sensitivity to several targeted therapies, including Vorinostat and Trametinib. Further prioritization analyses identified EEF1E1, ITGB3BP, and SPP1 as promising candidate biomarkers with potential diagnostic and prognostic relevance. The RCDI model effectively stratifies HCC patients based on RCD-related molecular features, providing a valuable tool for predicting survival and therapeutic responses. The identification of key genes offers new insights into the molecular mechanisms of HCC and potential therapeutic targets.

The study aims at investigating the function of macrophage-related genes (MRGs) within the bladder cancer immune microenvironment and exploring their potential value in prognosis prediction and therapeutic decision-making. This study integrated bladder cancer transcriptomic data from the TCGA and GEO databases along with single-cell RNA sequencing (scRNA-seq) data to systematically identify key MRGs. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and single-cell sequencing analysis served for screening for core MRGs. The results from LASSO Cox regression analysis were used for constructing a survival risk prediction model, together with the evaluation of the model's predictive accuracy. Besides, core MRGs were subjected to immune cell infiltration and drug sensitivity analyses for the elucidation of their roles in immune regulation and therapeutic response. Furthermore, key genes in the prognostic model were validated using PCR, Western blot, and immunohistochemistry. This study identified 11 core genes significantly associated with macrophages and developed a risk prediction model based on ANXA1, ST3GAL5, and VIM. The model demonstrated moderate predictive performance across all samples (AUC = 0.682), indicating potential utility for patient stratification. Immune analysis revealed that high-risk patients exhibited a distinctly immunosuppressive tumor microenvironment (TME), characterized by increased infiltration of M2 macrophages and neutrophils, along with a significant reduction in effector immune cells of CD8⁺ T cells and NK cells. Additionally, high-risk patients displayed greater sensitivity to targeted therapies but reduced sensitivity to conventional chemotherapy. According to in vitro and in vivo experiments, ST3GAL5 overexpression significantly promoted bladder cancer cell proliferation and tumor growth, underscoring its potential role in tumor progression. This study highlights the crucial impact of MRGs on the TME of bladder cancer and constructs a risk prediction model with moderate predictive performance that may assist in patient stratification, although further validation in independent cohorts is required.

A novel drug screening strategy based on non-invasive electrochemical detection of multiple tumor markers secreted by patient-derived microgel organoids

Early and accurate diagnosis remains a major challenge in cervical cancer management. This study aimed to identify reliable diagnostic biomarkers for cervical cancer by integrating bioinformatics and machine learning approaches and to further validate their biological relevance experimentally. Transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas were analyzed using differential expression analysis, weighted gene co-expression network analysis, and three machine learning algorithms to identify core genes. Diagnostic performance was evaluated using receiver operating characteristic curves and a nomogram model. Functional relevance was explored by drug sensitivity analysis, ssGSEA, immune infiltration analysis, and single-cell RNA sequencing. RT-qPCR validation was performed in 10 paired cervical cancer and adjacent normal tissues, while Western blotting was performed in three paired tissue samples. In vitro validation was conducted using SiHa and HeLa cells. Four genes, CCND1, TRIP13, MYBL2, and GNB4, were identified as potential diagnostic biomarkers, and the combined model showed superior diagnostic performance compared with any single gene (AUC = 0.989). Treatment with 3-methyladenine altered the expression of these genes, suggesting their potential association with PI3K/AKT-related pathway activity. Moreover, siRNA-mediated GNB4 knockdown suppressed cervical cancer cell proliferation and reduced PI3K and AKT phosphorylation, providing preliminary evidence for the functional involvement of GNB4 in PI3K/AKT pathway activation. CCND1, TRIP13, MYBL2, and GNB4 may serve as promising diagnostic biomarkers for cervical cancer. Their dysregulation was associated with PI3K/AKT pathway activity and may reflect molecular alterations involved in cervical cancer progression. In particular, GNB4 showed potential diagnostic relevance and preliminary functional significance, suggesting that it may represent a candidate biomarker and molecular target for further investigation.

Long noncoding RNAs (lncRNAs) are critical regulators of cancer progression, immune dynamics, and therapeutic response. MAGI2-AS3 has been implicated in multiple malignancies; however, its pan-cancer relevance and specific role in colorectal cancer (CRC) remain incompletely understood. MAGI2-AS3 expression was analyzed across cancers using bulk (TCGA, GTEx) and single-cell RNA-seq data. Functional enrichment, survival (Kaplan-Meier, Cox regression), diagnostic (ROC), immune infiltration (8 algorithms), immunotherapy response, drug sensitivity, and genomic/epigenetic analyses were performed. Expression was validated by RT-qPCR in 10 paired CRC tissues. MAGI2-AS3 expression exhibited marked heterogeneity across cancer types, with consistent downregulation in CRC and preferential enrichment in stromal and myeloid cell populations. Functional analyses linked MAGI2-AS3 to epithelial-mesenchymal transition, immune regulation, and cytokine signaling. While prognostic associations were context dependent across cancers, CRC demonstrated a distinct pattern: MAGI2-AS3 was consistently downregulated and showed robust diagnostic performance. Paradoxically, higher expression within tumors was associated with poorer survival outcomes and an inflamed yet immunosuppressive tumor microenvironment characterized by reduced response to immune checkpoint blockade, indicating a complex, compartment-specific role. Drug sensitivity and epigenetic analyses further underscored cancer-type-specific regulatory patterns, which were experimentally validated by RT-qPCR in CRC tissues. MAGI2-AS3 functions as a context-dependent regulator with strong diagnostic relevance and complex prognostic implications in colorectal cancer. Its involvement in immune modulation, therapeutic response, and tumor progression highlights its potential as a biomarker for precision oncology, warranting further mechanistic and clinical investigation.

Organelles have been reported to be closely associated with tumor development and progression, but their role in osteosarcoma (OS) remains largely unexplored. Organelle related genes (ORGs) associated with OS prognosis were identified using Cox regression analysis. A prognostic model was subsequently constructed through multivariate Cox regression analysis and validated using an independent dataset. Patients were stratified into high-risk and low-risk groups based on the median risk score. In addition, immune infiltration analysis, enrichment analysis and drug sensitivity evaluation were performed. Finally, in vitro experiments were conducted to validate the potential roles of ORGs in OS. We identified 3 ORGs (ACSS2, CLTCL1, and PLD3) that were significantly associated with OS prognosis. A novel 3 ORG signature was established, which effectively stratified patients into high-risk and low-risk groups with distinct survival outcomes. This signature served as an independent prognostic factor. The areas under the receiver operating characteristic (ROC) curve for the 1-, 4-, and 7-year survival rates were 0.66, 0.74, and 0.83, respectively. These findings were further validated using the independent GSE21257 dataset, where the corresponding ROC curve values for the 1-, 4-, and 7-year survival rates were 0.71, 0.80, and 0.68, respectively. Drug sensitivity analysis revealed differential responses to 4 drugs between the risk groups, with the 3 ORGs (ACSS2, CLTCL1 and PLD3) showing positive correlations with 2 drugs (BI_2536, Dactinomycin). Additionally, functional experiments confirmed the role of ACSS2 in OS cell behavior. This novel ORG signature not only provides a valuable tool for patient stratification but also offers insights into the biological processes driving OS progression and potential therapeutic targets.

Dysregulation in chemotaxis and activation of neutrophils may trigger cancer. Nevertheless, the function of neutrophils and their mechanism during the prognosis of pancreatic cancer (PC) remain unclear. From the databases of The Cancer Genome Atlas (TGCA) and GeneCards, the genes of tumor-associated neutrophils (TANs) were screened out leveraging the differential expression analysis. The constructed prognostic model for PC was analyzed through the least absolute shrinkage and selection operator (LASSO) regression and Cox univariate and multivariate regression. The dataset (GSE62452) provided by the Gene Expression Omnibus (GEO) database served as the validation cohort, and its potential mechanistic pathways and biological functions were analyzed leveraging Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The immune infiltration analysis, as well as the drug sensitivity prediction, were conducted. The gene expression in the prognostic model was checked through online databases, including Human Protein Atlas (HPA) and Tumor Immune Single-Cell Hub (TISCH). Finally, the genes were experimentally confirmed by immunohistochemistry (IHC) and qPCR in combination with clinical samples from patients with PC. AIM2, PSCA, IL18BP, and LIPE were four genes of TANs closely linked to the prognosis of PC. A model for the prognosis of PC was established utilizing these 4 genes. The AUC (area under the receiver operating characteristic curve (ROC curve)) values of this model for forecasting the survival rates of patients at 1, 2, and 3 years were 0.774, 0.841, and 0.953, respectively. The immune infiltration analysis revealed that resting dendritic cells (resting DCs), naive B cells, CD8T cells, as well as follicular helper T cells, were highly infiltrated among patients suffering from PC. According to the drug sensitivity analysis, the high-risk pancreatic ductal adenocarcinoma (PAAD) group had a significantly higher inhibitory concentration 50 (IC50) for dactolisib, docetaxel, gemcitabine, and ulixertinib compared to the group by patients with low-risk PAAD. The results of IHC and qPCR confirmed those of bioinformatics analysis. New TANs-related biomarkers have been found that effectively forecast the prognosis of patients suffering from PAAD.

Lung adenocarcinoma (LUAD) is one of the most severe malignant tumors. Phosphoinositides metabolism (PIM) plays an important role in maintaining the normal life activities of the organism and regulating tumor development. This study aimed to comprehensively investigate the association between LUAD and PIM. Data on LUAD and PIM-related genes (PIM-RGs) were sourced from public databases. Differential expression, univariate Cox regression analyses, and machine learning were conducted to identify prognostic genes. A risk model was subsequently developed, and LUAD patients were categorized into a high-risk group (HRG) and a low-risk group (LRG). Independent prognostic factors for LUAD were identified, and a nomogram was constructed. Functional enrichment, tumor microenvironment, mutations, and drug sensitivity analyses were also conducted to investigate the molecular mechanisms underlying LUAD. Additionally, single-cell RNA sequencing (scRNA-seq) data analysis was employed to identify key cells and clarify the dynamics of prognostic genes' expression. Ultimately, prognostic gene expression was investigated in clinical samples. MTMR7, GDPD1, MTMR4, and MTMR10 were recognized as prognostic genes. The risk model and nomogram (incorporating risk score and Stage) had good predictive performance. Notably, LUAD's malignant progression might be closely associated with biological processes including cellular protein synthesis, abnormal activation of neuroactive ligand-receptor interactions, and anti-tumor immune responses. Additionally, there was a general positive correlation between the differentially infiltrated immune cells of HRG and LRG. Moreover, TP53 and TTN had relatively high mutation frequencies in both HRG and LRG, and 142 drugs exhibited differential sensitivity between the 2 groups. Interestingly, epithelial cells were identified as LUAD's key cell type, with prognostic gene expression showing dynamic changes as these cells differentiated. Consistently, compared with the control group, GDPD1 and MTMR4 were upregulated, MTMR10 was downregulated, and MTMR7 showed no statistical difference but a certain upward trend in the LUAD group. This study identified 4 prognostic genes and constructed an effective risk model, providing a new perspective on the treatment of LUAD.

Despite significant improvements in the overall survival of Wilms tumor (WT), a subset of patients still experiences poor outcomes. Programmed cell death (PCD) pathways are pivotal in cancer progression. A deeper understanding of their roles in WT is crucial for harnessing these mechanisms to optimize risk stratification. Key tumor-associated genes were identified through limma differential analysis and WGCNA, and subsequently integrated with 12 distinct PCD patterns. TARGET-WT transcriptomic data was divided into training and validation sets to construct and validate a risk stratification model. It was subsequently integrated with clinical information to build a comprehensive prediction model. Immune infiltration and drug sensitivity analyses were performed. External validation was performed using scRNA-seq data from GSE200256. Key tumor-associated genes were enriched in multiple PCD pathways. The risk stratification model was constructed using 4 genes selected via the machine learning algorithm, stratifying the cohort into high- and low-risk groups. In the overall WT cohort, the high-risk group exhibited worse prognosis, with 1-, 3-, and 5-year AUC values of 0.820, 0.721, and 0.728, respectively. DCA demonstrated the superior predictive accuracy of the comprehensive prediction model. The high-risk group showed lower infiltration of TH17 cells and increased sensitivity to paclitaxel and sorafenib. Finally, the expression landscape of hub genes was validated in the single-cell dataset. These results highlight a critical role for PCD genes in the progression and immune regulation of WT. Targeting these genes offers a promising avenue for improving clinical management of patients identified as high-risk.

Skin cutaneous melanoma (SKCM) is a highly aggressive malignancy with rising global incidence and mortality. Despite advances in immunotherapy and targeted therapies, treatment resistance remains a challenge, necessitating novel prognostic biomarkers and therapeutic strategies. Cuproptosis, a copper-dependent form of regulated cell death, and immune-related pathways have emerged as critical players in tumor progression. However, their combined prognostic potential in SKCM remains unexplored. Here, we constructed a cuproptosis-immune-related gene signature to predict SKCM prognosis and guide therapy. Using the TCGA database, we identified 474 cuproptosis-related immune genes through Pearson correlation analysis. By integrating the GTEx database, differential expression analysis revealed that 194 of these genes were significantly dysregulated in SKCM. Univariate Cox and LASSO regression analyses established a 12-gene prognostic model (C3AR1, CCL8, CCR1, CTLA4, HLA-DRB1, IFIH1, IL2RA, IRF9, KIR2DL4, TLR1, TNFRSF21, XCL2), stratifying patients into high- and low-risk groups. The model demonstrated robust predictive accuracy in training and validation cohorts. High-risk patients exhibited poorer survival, reduced immune infiltration, suppressed checkpoint expression, and lower tumor mutational burden (TMB), suggesting an immunosuppressive microenvironment. Conversely, low-risk patients showed enhanced immune infiltration, higher TMB, and increased checkpoint-related gene expression, suggesting an immune-inflamed but functionally restrained phenotype with potential relevance to immune checkpoint blockade. Drug sensitivity analysis revealed high-risk patients may benefit more from targeted therapies. A nomogram integrating risk scores and clinical factors further improved prognostic prediction, with calibration curves demonstrating strong concordance between predicted and observed survival probabilities. Single-cell RNA sequencing illustrated the cellular distribution of model genes, and functional experiments demonstrated that XCL2 suppresses melanoma cell proliferation, migration, and invasion. This study develops and validates a cuproptosis-immune integrated prognostic signature for SKCM, providing a framework to link cuproptosis-associated biology with immune microenvironmental features, risk stratification, and potential therapeutic decision-making.

The aggressive cancer known as Esophageal Squamous Cell Carcinoma (ESCC) has a dismal prognosis. Epigenetic changes such as demethylation have a significant impact on ESCC development and progression. WGCNA and differential expression analysis of GEO datasets identified tumorrelated demethylation genes. Random Survival Forest (RSF), univariate Cox regression, and LASSO-Cox models were employed to further screen prognostic genes and establish risk prediction models. Drug sensitivity and immune infiltration analyses were used to evaluate therapeutic implications. Mendelian Randomization (MR) assessed the genetic causality of key genes. Single-cell RNA sequencing elucidated cellular heterogeneity, intercellular communication, and differentiation trajectories in the tumor microenvironment. Finally, qPCR validated key gene expression in both ESCC tumor tissues and adjacent normal tissues. Integrating co-expression with differential expression analyses enabled the identification of 150 demethylation genes associated with tumors. Six key prognostic genes (SERPINH1, PLAU, ANO1, RAB25, MAGEA4, and COL2A1) were selected to develop a risk prediction model, which showed improved accuracy after integrating clinical variables Age and Stage. Risk scores positively correlated with tumor stage and patient age, with higher scores predicting increased sensitivity to cisplatin, docetaxel, and vinorelbine. Immune infiltration analysis revealed reduced neutrophil levels associated with key gene expression. MR identified PLAU and RAB25 as causally linked to ESCC. Single-cell transcriptome and cell communication analyses highlighted squamous epithelial cells and altered LAMININ signaling in tumors. qPCR and expression analysis verified the expression of key genes. This study identifies demethylation-driven prognostic genes as possible targets for treatment and biomarkers for precision management of ESCC. Six prognostic genes, particularly RAB25 and PLAU, influence ESCC progression and immune microenvironment remodeling.

Studies have shown that PANoptosis is increasingly involved in cancer and cancer treatment. The Cordycepin has also been found to be involved in the development of various cancers. However, relevant research on both in lung adenocarcinoma (LUAD) remains relatively scarce. The present study aims to identify potential prognostic genes in LUAD and elucidate their impact on the prognosis of LUAD patients, with the goal of providing novel insights into the therapeutic strategies for this disease. The target genes for Cordycepin and transcriptomic datasets for LUAD were retrieved from public databases, and PANoptosis-related genes were retrieved from literature. This study employed differential expression analysis, consensus clustering analysis, weighted gene co-expression network analysis (WGCNA), and Cox regression analysis. This study identified potential biomarkers. The backpropagation neural networks (BPNNs) were constructed using these biomarkers. Furthermore, the study delved into underlying biological mechanisms through enrichment analysis. Additional analyses were performed to evaluate functional pathways, immune infiltration, and drug sensitivity in different risk individuals. Finally, clinical samples were collected and biomarker expression was validated using RT-qPCR. A total of 8 biomarkers including SLC2A1, SMS, CCNA2, CDC25C, RNASE1, NR3C2, CAT, and ADA were identified in this study. Among these, SLC2A1, SMS, CCNA2, CDC25C, and ADA were significantly upregulated in clinical disease samples, while RNASE1, NR3C2, and CAT were significantly downregulated. The GSEA results indicated that the biomarkers were primarily enriched in mismatch repair and proteasome. There were 10 differential immune cells (Macrophages M0, Monocytes, activated dendritic cells, etc.) between two subgroups detected by the CIBERSORT algorithm. Meanwhile, 18 differential immune cells including Activated CD4+ T cells, Memory B cells, Natural killer T cells, etc. were identified using the ssGSEA algorithm. The analysis of drug sensitivity showed significant differences in 22 drugs between the two risk groups. Our results suggested that 8 biomarkers including SLC2A1, SMS, CCNA2, CDC25C, RNASE1, NR3C2, CAT, and ADA were associated with PANoptosis in LUAD. These findings may provide supportive evidence for the prognosis prediction and treatment of LUAD. However, the involvement of these genes in PANoptosis is currently only a hypothesis based on bioinformatics analysis and requires further experimental validation in the future.

可利霉素是一种在医药领域广泛使用的大环内酯类抗生素，主要成分为3种不同的螺旋霉素衍生物。然而随着相关研究的深入，近期发现可利霉素以及多种不同结构的螺旋霉素衍生物都具有一定的抗肿瘤活性，说明利用螺旋霉素作为母核，进行不同的取代基修饰，可能会得到更好的抗肿瘤活性药物。虽然螺旋霉素有着成为抗肿瘤药物的潜力，但是关于其作用靶点及完整机制的研究依然欠缺。为了揭示其抗肿瘤作用机制，本研究采用了基于活性的蛋白质组分析（ABPP）策略，设计并合成了一种高活性螺旋霉素衍生物正己酰螺旋霉素（h-SPM），进而依据其结构合成了一种结构相似且带有适用于ABPP实验官能团的活性分子探针。利用该探针与细胞蛋白质共孵育，使两者结合，从而捕获h-SPM潜在的药物靶点，随后对探针结合的蛋白质进行分离纯化与质谱分析，获得详细的靶点蛋白质信息。进一步通过基因本体（GO）分析挖掘这些蛋白质的性质和功能，得到明确的功能信息，并据此筛选出若干潜在靶点蛋白质。通过上述方法，从蛋白质谱的结果中鉴定出包括淀粉样前体蛋白（APP）、低密度脂蛋白受体（LDLR）在内的多个h-SPM潜在作用靶点，并采用免疫印迹（Western Blotting）方法确定这些蛋白质对h-SPM产生了响应。随后，通过短发夹RNA（shRNA）介导的蛋白质敲低及细胞染色等实验，证明蛋白质APP在该药物发挥作用的过程中扮演了关键角色，初步揭示了该类药物的抗肿瘤作用机制。本研究不仅建立了适用于螺旋霉素类化合物的靶点筛选方法，也为该类药物的后续开发提供了关键靶点线索与理论依据。

Clear cell renal cell carcinoma (ccRCC) exhibits significant metabolic alterations. Protein succinylation, a metabolite-induced post-translational modification, plays a vital role in cellular metabolism and tumor biology. To characterize the succinylation-associated transcriptional signature and its clinical relevance in ccRCC. We developed a succinylation-associated transcriptional score based on a literature-curated 20-gene panel using the TCGA-KIRC RNA-seq dataset, with prognostic validation in two independent cohorts (E-MTAB-1980 and CPTAC). The tumor immune microenvironment was analyzed via ESTIMATE, CIBERSORT, and ssGSEA. Cellular localization of the score was investigated using single-cell and spatial transcriptomics. Functional enrichment, drug response analyses, and qPCR validation were also performed. Tumor tissues demonstrated significantly reduced succinylation-associated transcriptional scores compared to normal counterparts, with higher scores correlating with improved clinical outcomes. Multivariate analyses supported the independent prognostic value of the succinylation-associated transcriptional score for overall survival in the TCGA and validation datasets. Interestingly, low-score tumors exhibited a transcriptionally "immune-hot" phenotype, characterized by enhanced immune cell infiltration and elevated immune checkpoint expression. Single-cell and spatial transcriptomic analyses suggested that tumor cells primarily contributed to the succinylation-associated transcriptional score. Functional assessments revealed that high scores were associated with oxidative phosphorylation and fatty acid metabolism, while low scores correlated with pro-tumorigenic signaling pathways. Computational drug sensitivity analysis identified exploratory associations that may inform future therapy stratification based on succinylation profiles. The succinylation-associated transcriptional score represents a promising biomarker candidate in ccRCC, showing associations with prognosis and key metabolic-immune features. Our study underscores the role of succinylation in ccRCC biology and provides a framework for metabolic subtyping that may inform future biological and clinical stratification studies.

BackgroundEsophageal cancer is a highly aggressive malignancy with poor prognosis and limited molecular markers for effective risk stratification and therapeutic guidance. Mitochondrial stress–related pathways are increasingly recognized as important regulators of tumor progression and immune modulation; however, their clinical relevance in esophageal cancer remains insufficiently characterized.MethodsTranscriptomic and clinical data of esophageal cancer were obtained from public databases. A predefined mitochondrial stress–related gene set was analyzed using LASSO Cox regression to construct a prognostic model. Survival analysis, functional enrichment, immune regulatory profiling, and immune cell infiltration analyses were subsequently performed. Single‐cell RNA sequencing data from the GSE160269 cohort were used to localize gene expression within the tumor microenvironment. In addition, pan‐cancer drug sensitivity analyses were conducted using pharmacogenomic datasets.ResultsATP1A1 was identified as the only LASSO‐selected gene demonstrating consistent prognostic significance, with higher expression associated with improved survival outcomes. Functional analyses indicated that ATP1A1 expression was mainly associated with metabolic pathways and negatively correlated with epithelial–mesenchymal transition, invasion, and quiescence states. Immune analyses showed that ATP1A1 expression was associated with heterogeneous immunomodulatory patterns, differences in immune cell infiltration, and reduced activity across several steps of the cancer immunity cycle. Single‐cell analysis demonstrated that ATP1A1 expression was not only preferentially enriched in malignant epithelial cells but also detectable in stromal and immune cell populations. Drug sensitivity analyses suggested that ATP1A1 expression was associated with differential responses to multiple therapeutic agents. Experimental validation further showed that ATP1A1 knockdown altered tumor cell behavior and increased inflammatory gene expression in esophageal cancer cells.ConclusionsATP1A1 represents a prognostically relevant factor linked to tumor biological characteristics, immune microenvironment features, and therapeutic response heterogeneity in esophageal cancer.