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At least 19 recordsLinked to original sources
PubMed2026

An oxidative stress - and immunotherapy-related six-gene signature defines immune subtypes and predicts prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Oxidative stress and the tumor immune microenvironment jointly shape hepatocellular carcinoma (HCC) progression and response to immunotherapy, yet integrated biomarkers linking these processes are lacking. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to identify oxidative stress- and immunotherapyrelated differentially expressed genes (OSIRDEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression were used to construct a prognostic signature. Consensus clustering, TIDE, CIBERSORT and ssGSEA characterized immune phenotypes. Somatic mutation, copy-number and drug-response data were integrated to assess genomic alterations and drug sensitivity. Expression of model genes was validated by qRT-PCR and western blotting in HCC cell lines. RESULTS: We identified 24 OSIRDEGs enriched in cell-cycle and mitotic pathways. WGCNA intersection yielded 18 module genes, from which a six-gene signature (BUB1B, CDKN2A, CENPE, HMMR, PTTG1, SPP1) was derived. The signature robustly stratified patients into high- and low-risk groups with significantly different progression-free and disease-free survival in both TCGA-LIHC and GSE14520. Based on signature expression, two molecular subtypes were defined, exhibiting distinct survival, immune landscapes and predicted immunotherapy responsiveness. Model genes harbored recurrent alterations and showed significant correlations with anticancer agents. All six genes were upregulated at mRNA and protein levels in metastatic HCC cell lines versus normal hepatocytes. CONCLUSIONS: We systematically explored the landscape of OSIRDEGs in HCC, and proposed a validated six-gene signature that refines prognostic stratification, delineates immunerelevant HCC subtypes and highlights candidate biomarkers for therapeutic selection and mechanistic investigation.

Humans↗
PubMed2024

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer.

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Humans↗
PubMed2025

MYC and p53 Alterations Cooperate through VEGF Signaling to Repress Cytotoxic T-cell and Immunotherapy Responses in Prostate Cancer.

UNLABELLED: Patients with castration-resistant prostate cancer (CRPC) are generally unresponsive to tumor-targeted treatments and immunotherapies. Genetic alterations acquired during the evolution of CRPC may affect antitumor immunity and immunotherapy responses, which could inform personalized therapeutic strategies. Using our innovative electroporation-based mouse models, we generated distinct genetic subtypes of CRPC found in patients and uncovered unique immune microenvironments. Specifically, mouse and human prostate tumors with MYC amplification and p53 disruption had weak cytotoxic lymphocyte infiltration and an overall dismal prognosis. MYC and p53 cooperated to induce tumor-intrinsic secretion of VEGF, which signaled through VEGFR2 expressed on CD8+ T cells to directly inhibit T-cell migration and effector functions. Targeting VEGF-VEGFR2 signaling in vivo remodeled the immunosuppressive prostate tumor microenvironment, leading to CD8+ T-cell-mediated primary tumor and metastasis growth suppression and significantly increased overall survival in MYC- and p53-altered CRPC. VEGFR2 blockade also led to the induction of PD-L1 in tumors and produced antitumor efficacy in combination with PD-L1 immune checkpoint blockade in multiple preclinical CRPC mouse models. Thus, these results identify a genetic mechanism of immunosuppression through VEGF signaling in prostate cancer that can be targeted to reactivate immune and immunotherapy responses in an aggressive subtype of CRPC. SIGNIFICANCE: VEGFR2 blockade inhibits VEGF-mediated T-cell suppression and potentiates the effects of PD-L1 immune checkpoint blockade to treat castration-resistant prostate cancer driven by MYC and p53 alterations.

Male↗
PubMed2025

ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response.

BACKGROUND: Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability. METHODS: Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis. RESULTS: Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets. CONCLUSIONS: Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.

Humans↗
PubMed2025

Multimodal deep learning for immunotherapy response prediction and biomarker discovery in non-small cell lung cancer.

OBJECTIVE: Immunotherapy has emerged as a promising treatment for advanced non-small cell lung cancer (NSCLC), but accurately predicting which patients will benefit from it remains a major clinical challenge. To address this, we aim to develop a novel multimodal method, DeepAFM, that integrates histopathology, genomic features, and clinical information to predict patient responses to anti-PD-(L)1 immunotherapy. MATERIALS AND METHODS: A total of 93 patients with advanced NSCLC were included in this study. Histopathological whole-slide images were processed using a self-supervised VQVAE2 for representation learning. PCA and K-means clustering were then applied for dimensionality reduction and feature grouping. Key regions of interest were visualized through permutation importance evaluation and color-coding techniques. The extracted histopathological features, along with genomic alterations and clinical variables, were integrated into the DeepAFM multimodal prediction model. RESULTS: The DeepAFM achieved a high predictive performance with an area under the curve (AUC) of 0.77 (95% confidence interval: 0.69-1.00). Attention-based heatmaps revealed that the model could identify critical pathological patterns, genomic mutations, and clinical indicators associated with patient responses to immunotherapy. DISCUSSION: The integration of multimodal data enabled the model to capture complex interactions among pathology, genomics, and clinical characteristics, enhancing the interpretability and predictive power of immunotherapy response prediction. The visualization techniques facilitated the identification of biologically meaningful features and potential biomarkers. CONCLUSION: This study demonstrates the effectiveness of the DeepAFM in predicting responses to immunotherapy in advanced NSCLC. The approach not only improves prediction accuracy but also provides valuable insights for personalized treatment strategies and biomarker discovery.

Humans↗
PubMed2026

Plasma cell-CD8+ T cell co-enrichment distinguishes immunotherapy-responsive hepatocellular carcinoma subtypes.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterised by significant racial disparities in incidence and outcomes, yet whether these reflect distinct tumour biology or differential distribution of molecular subtypes among immunotherapy patients remains unclear. METHODS: We characterised molecular heterogeneity among 46 patients with HCC of differing background population from the NCI-CLARITY cohort receiving immune checkpoint inhibitor therapy, using transcriptomic and genomic profiling, with validation across multiple independent cohorts. RESULTS: Differential expression analysis comparing African American versus non-African American patients identified 126 genes, of which 55 demonstrated tumour-specific expression across independent validation cohorts with paired tumour-normal samples. Consensus clustering revealed two molecular subtypes with no significant race association, indicating these clusters capture tumour-intrinsic biology rather than ancestry. The genomic landscape showed minimal differences between subtypes. A prognostic signature derived from these expression profiles demonstrated significant risk stratification in the NCI-CLARITY cohort and TCGA-LIHC, but not in Asian cohorts, suggesting population-specific applicability. Immune deconvolution revealed that the two subtypes represent distinct immune microenvironments: one subtype exhibited markedly elevated plasma cell infiltration with strong plasma cell-CD8+T cell correlation suggesting coordinated adaptive immunity, along with elevated tertiary lymphoid structure signatures. The other subtype showed regulatory T cell-macrophage correlation and enrichment for immune-excluded phenotypes. The immune-enriched subtype trended towards higher immunotherapy response rates. CONCLUSIONS: Molecular heterogeneity in HCC reveals distinct tumour-immune ecosystems that transcend racial classification. Tumour immune heterogeneity in HCC reflects distinct molecular patterns, with immune hot tumours characterised by elevated tertiary lymphoid structure signatures and enriched plasma cell and CD8+T cells. These patterns may serve as prognostic biomarkers for immunotherapy patient stratification and demonstrate the value of diverse cohort representation in identifying clinically relevant therapeutic targets.

Gastrointestinal Cancer↗
PubMed2026

A model of cellular proliferation and mitochondrial biogenesis predicts prognosis and immunotherapy response in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD), which is the leading subtype of non-small cell lung cancer (NSCLC), poses considerable difficulties in accurate prognostic assessment and targeted therapeutic options. Cell proliferation-related genes (CPGs) and mitochondrial biogenesis-related genes (MBGs) play critical roles in tumor metabolic reprogramming; however, their prognostic value and molecular mechanisms in LUAD are poorly understood. This study aims to construct a CPG/MBG-based prognostic risk model for LUAD, evaluate its clinical utility in predicting prognosis and immunotherapy response, and experimentally validate the functional role of key model genes in LUAD progression. METHODS: By utilizing The Cancer Genome Atlas (TCGA)-LUAD and GSE72094 datasets, this investigation formulated a risk scoring model through differential expression screening combined with least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. The molecular characteristics and clinical implications of the risk model were investigated via immune microenvironment evaluation, genomic alteration analysis, and drug sensitivity prediction. The functional contributions of key genes were further substantiated using quantitative reverse transcription polymerase chain reaction (qRT-PCR), commercial assay kits, the JC-1 fluorescent probe, the Cell Counting Kit-8 (CCK-8), Transwell invasion assays, and wound healing assays. RESULTS: A risk model based on seven CPGs and MBGs (PLK1, HMMR, CYP27A1, LDHA, NPAS2, KRT17, CIDEC) showed reliable predictive performance in both GSE72094 and the TCGA-LUAD cohorts. Enhanced tumor heterogeneity and an immunosuppressive microenvironment were observed in the high-risk group. Drug sensitivity analysis indicated that the risk model could guide personalized treatment strategies; for instance, high-risk patients showed increased susceptibility to agents such as docetaxel and 5-fluorouracil. In vitro experiments demonstrated that the key gene CIDEC exhibited upregulated expression in LUAD tissues and cells. Knockdown of CIDEC led to enhanced cellular energy metabolism and increased mitochondrial membrane potential, while also effectively suppressing cell invasion, proliferation, and migration. CONCLUSIONS: The established MBGs/CPGs prognostic model provides a novel tool for stratified treatment planning in LUAD, underscoring the crucial roles of cellular proliferation and mitochondrial biogenesis in tumor progression. Functional validation of CIDEC offers experimental support for the development of potential therapeutic strategies.

Lung adenocarcinoma (LUAD)↗
PubMed2026

Pan-cancer single-cell atlas of immunotherapy response identifies ZNF385A as a regulator of immune evasion in small cell lung cancer.

Although immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape of solid tumors, response rates in patients with small cell lung cancer (SCLC) remain limited, and acquired resistance is highly prevalent. The underlying mechanisms of this immunotherapy resistance remain to be fully elucidated. Clinically, SCLC typically manifests as an "immune-cold" tumor, characterized by a low abundance of CD8+ T cell infiltration and the rare formation of tertiary lymphoid structures (TLS). While DNA damage repair (DDR) is closely linked to innate immune responses, how DDR networks orchestrate the SCLC immune microenvironment remains obscure. In this study, we integrated single-cell transcriptomic data (comprising 344,447 high-quality cells) from six cancer types (BCC, CRC, HCC, HNSCC, iCCA, and SCLC). Our comparative analysis revealed a fundamental depletion of TLS-associated cellular subpopulations (e.g., CXCL13+ CD8+ T cells, HLA-DRB5+ B cells, and CXCL9+ dendritic cells) in SCLC, which was significantly correlated with aberrant DDR activity. Through high-dimensional weighted gene co-expression network analysis (hdWGCNA), we identified ZNF385A as the core hub gene within the DDR-associated module. ZNF385A is highly expressed in SCLC and is associated with poorer prognosis. In vitro, ZNF385A depletion suppressed SCLC cell proliferation and induced apoptosis, accompanied by R-loop accumulation and activation of cGAS-STING signaling, indicating a potential link between ZNF385A, genomic stability and tumor-intrinsic innate immune signaling. Collectively, these findings identify ZNF385A as a potential regulator associated with TLS deficiency and immune evasion in SCLC.

Immunotherapy resistance↗
PubMed2025

HDAC inhibition unlocks tumor plasticity and enhances immunotherapy response in Myc-Driven Small Cell Lung Cancer.

Small Cell Lung Cancer (SCLC) is a highly aggressive malignancy, accounting for approximately 15% of all lung cancer cases. Characterized by low immunogenicity, SCLC may utilize epigenetic mechanisms to evade immune detection. Here, we demonstrate that entinostat, a class I histone deacetylase inhibitor (HDACi) upregulates immune-related genes in human SCLC cells. In vivo, we confirmed entinostat treatment increased expression of immunecheckpoint ligands and antigen presentation machinery in Myc-driven tumors in a Rb1/Trp53/MycT58A (RPM) SCLC mouse model, while shifting tumors from a neuroendocrine(NE)-high to a NE-low phenotype. Notably, combining entinostat with anti-PD-1 immunotherapy significantly enhances T-cell infiltration, suppresses tumor growth, and prolongs survival in RPM allograft models. These findings underscore the potential of entinostat to reprogram the immunological landscape and NE status of SCLC, enhance immune checkpoint blockade efficacy, and improve therapeutic outcomes.

anti-PD-1 therapy↗
PubMed2026

Secreted protein circuits in the gastrointestinal tumour microenvironment: determinants of immunotherapy response and resistance.

Immune checkpoint blockade has transformed treatment in selected gastrointestinal (GI) cancers, yet primary resistance, incomplete responses and acquired resistance remain common. This heterogeneity is not explained by tumour-cell genomics alone; extracellular signalling programmes within the tumour microenvironment can determine immune recruitment, access and adaptation to therapy. The tumour secretome-including cytokines, chemokines, growth factors, complement components, matricellular proteins, soluble checkpoint molecules and extracellular-vesicle-associated cargo-regulates immune-cell recruitment, exclusion, suppression, tertiary lymphoid structure formation and exhaustion across anatomical and molecular contexts. Across gastric and esophageal cancers, colorectal cancer, pancreatic ductal adenocarcinoma, hepatocellular carcinoma and biliary tract cancers, recurrent suppressive circuits include TGF-β, VEGF, CXCL12-CXCR4, CXCL8/IL-8-CXCR1/2, CCL2-CCR2, CSF1-CSF1R, IL-6-family cytokines, SPP1/osteopontin, periostin, galectins, DKK1, MIF, complement and soluble or vesicular PD-L1. Conversely, CXCL9/10/11-CXCR3 signalling and CXCL13-associated tertiary lymphoid structures characterise immune-permissive states that can support checkpoint responsiveness. We organise these circuits into four overlapping functional modules-myeloid-enriched, fibroblast-driven exclusion, angiogenic-immunosuppressive and immune-permissive-and apply a four-level evidence hierarchy that separates clinical validation from mechanistic inference. Clinically useful secretome biomarkers will therefore need to integrate cellular source, spatial localisation, receptor context, temporal dynamics and linkage to actionable immune-state transitions.

Humans↗
PubMed2024

Case report: response to immunotherapy and association with the fh gene in hereditary leiomyomatosis and renal cell cancer-associated renal cell cancer.

Hereditary leiomyomatosis and renal cell cancer (HLRCC) is a rare autosomal dominant syndrome caused by a germline mutation in the fumarate hydratase (FH) gene that manifests with cutaneous leiomyomas, uterine fibroids, and renal cell cancer (RCC). Patients with HLRCC-associated RCC (HLRCC-RCC) have aggressive clinical courses, but there is no standardized therapy for advanced HLRCC-RCC. In this study, we described a case of aggressive HLRCC in a 33-year-old female who exhibited a novel heterozygous germline insertion mutation in exon 8 of the FH gene (c.1126 C > T; p.Q376*). The patient underwent laparoscopic resection of the right kidney, but metastases appeared within 3 months after surgery. Histological staining of the resected tumor revealed high expression levels of programmed cell death-ligand 1 (PD-L1). Therefore, the patient was treated with immunotherapy. The patient achieved a partial response to immunotherapy, and the treatment of metastatic lesions has continued to improve. A thorough literature review pinpointed 76 historical cases of HLRCC-RCC that had undergone immunotherapy. From this pool, 46 patients were selected for this study to scrutinize the association between mutations in the FH gene and the effectiveness of immunotherapy. Our results indicate that immunotherapy could significantly improve the overall survival (OS) of patients with HLRCC-RCC. However, no influence of different mutations in the FH germline gene on the therapeutic efficacy of immunotherapy was observed. Therefore, our study suggested that immunotherapy was an effective therapeutic option for patients with HLRCC regardless of the type of FH germline mutation.

Humans↗
PubMed2026

Identification and evaluation of glutamine-related gene characteristics based on multi-omics to predict the prognosis of patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC), a prevalent malignancy of the gastrointestinal tract, ranks among the leading causes of cancer-related morbidity and mortality. Its clinical course is marked by high fatality and poor prognosis. Elucidating the mechanisms underlying CRC initiation and recurrence is therefore critical for identifying novel therapeutic targets. METHODS: This study incorporated two datasets, TCGA-CRC and GSE17537. A total of 84 glutamine metabolism-related genes (GMRGs) were identified, and differential expression analysis was conducted using the TCGA-CRC dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to determine gene modules most strongly associated with GMRG scores. Single-cell RNA sequencing (scRNA-seq) was utilized to characterize key cellular clusters and to identify differentially expressed genes (DEGs) between high and low glutamine metabolism (GM) groups. Overlapping GMRGs were visualized using the ggVennDiagram package in R. A CRC risk prediction model was developed through Cox proportional hazards and LASSO regression analyses, with performance evaluated by ROC curves. Cell type enrichment across 64 immune and stromal populations was assessed via xCell, and intergroup differences were tested using the Wilcoxon rank-sum test. TIDE scores were used to estimate immunotherapy responsiveness, while oncoPredict facilitated drug sensitivity profiling. PCOLCE2 expression in CRC was validated by RT-qPCR and Western blotting. Its functional role was examined through CCK-8 assays, invasion and migration tests, flow cytometry, and glutamate quantification. RESULTS: ScRNA-seq analysis identified two key cell populations and 437 DEGs associated with GM status. WGCNA pinpointed the MEgreen module as most significantly correlated with GMRG scores, encompassing 1075 genes. Integration of DEGs, module genes, and GM-related DEGs yielded 60 candidate genes for downstream analysis. A GMRG-based prognostic model comprising six genes (SRPX, CXCL1, GPX3, PCOLCE2, CLU, SEMA3E) demonstrated strong predictive performance. Prognostic gene expression correlated with immune and stromal infiltration patterns, as indicated by Spearman correlation analysis. The high-risk group exhibited diminished predicted response to immunotherapy (TIDE scores). Drug sensitivity analysis identified four compounds—Dasatinib-51, WH-4-023-56, TWS-119-366, and LDN-193189-478—with elevated efficacy in high-risk CRC cases. PCOLCE2 expression was significantly reduced in CRC tissues. Functional assays revealed that PCOLCE2 knockdown did not substantially affect cell proliferation but significantly impaired invasion and migration in CRC cells, increased apoptosis, and suppressed both glutamine uptake and glutamate production—highlighting its oncogenic role. CONCLUSION: Six GMRGs—SRPX, CXCL1, GPX3, PCOLCE2, CLU, and SEMA3E—were identified as key components of a robust prognostic model for CRC. These findings offer valuable insights into CRC pathogenesis and potential therapeutic strategies. Notably, this study provides the first evidence implicating PCOLCE2 as a tumor-promoting factor in CRC.

Glutamine↗
PubMed2026

SHMT2: a Metabolic and Immune Biomarker of Aggressive Lung Adenocarcinoma.

Serine/glycine-one-carbon (SGOC) metabolism is frequently altered in lung adenocarcinoma (LUAD), but its relationship to tumor behavior and predicted immunotherapy responsiveness remains incompletely defined. Metabolomic profiling of 23 paired LUAD and adjacent normal lung tissues was performed using internal extractive electrospray ionization mass spectrometry. Transcriptomic and clinical data from The Cancer Genome Atlas LUAD cohort (TCGA-LUAD) were analyzed to assess SHMT2 expression, prognosis, differentially expressed genes, and immune-related features. Predicted response to immune checkpoint blockade was evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA), and drug sensitivity was inferred using oncoPredict. Single-cell RNA-seq data were used to examine the cellular distribution of SHMT2. Experimental validation included quantitative reverse-transcription PCR (RT-qPCR), western blotting, Human Protein Atlas (HPA) immunohistochemistry, and short hairpin RNA (shRNA)-mediated SHMT2 knockdown followed by proliferation, wound-healing and colony formation assays. Metabolomic analysis identified glycine, serine, and threonine metabolism as a prominently altered pathway in LUAD. SHMT2 was upregulated in LUAD and associated with worse overall survival and adverse clinicopathological features. SHMT2-high tumors displayed enrichment of cell-cycle and SGOC-related transcriptional programs, lower immune and stromal scores, and reduced predicted responsiveness to immunotherapy. Single-cell analysis showed relative enrichment of SHMT2 expression in B cell populations. In vitro, SHMT2 was overexpressed in LUAD cells, and its knockdown suppressed proliferation, migration, and clonogenic growth. Collectively, SHMT2 is associated with SGOC metabolic reprogramming, aggressive tumor phenotypes, and an immune-disadvantaged state in LUAD, supporting its potential relevance as a biomarker; therapeutic targeting requires additional pharmacologic and in vivo validation.

Humans↗
PubMed2024

Integrated bioinformatics analysis and experimental validation reveal the relationship between ALOX5AP and the prognosis and immune microenvironment in glioma.

BACKGROUND: Treatment of gliomas, the most prevalent primary malignant neoplasm of the central nervous system, is challenging. Arachidonate 5-lipoxygenase activating protein (ALOX5AP) is crucial for converting arachidonic acid into leukotrienes and is associated with poor prognosis in multiple cancers. Nevertheless, its relationship with the prognosis and the immune microenvironment of gliomas remains incompletely understood. METHODS: The differential expression of ALOX5AP was evaluated based on public Databases. Kaplan-Meier, multivariate Cox proportional hazards regression analysis, time-dependent receiver operating characteristic, and nomogram were used to estimate the prognostic value of ALOX5AP. The relationship between ALOX5AP and immune infiltration was calculated using ESTIMATE and CIBERSORT algorithms. Relationships between ALOX5AP and human leukocyte antigen molecules, immune checkpoints, tumor mutation burden, TIDE score, and immunophenoscore were calculated to evaluate glioma immunotherapy response. Single gene GSEA and co-expression network-based GO and KEGG enrichment analysis were performed to explore the potential function of ALOX5AP. ALOX5AP expression was verified using multiplex immunofluorescence staining and its prognostic effects were confirmed using a glioma tissue microarray. RESULT: ALOX5AP was highly expressed in gliomas, and the expression level was related to World Health Organization (WHO) grade, age, sex, IDH mutation status, 1p19q co-deletion status, MGMTp methylation status, and poor prognosis. Single-cell RNA sequencing showed that ALOX5AP was expressed in macrophages, monocytes, and T cells but not in tumor cells. ALOX5AP expression positively correlated with M2 macrophage infiltration and poor immunotherapy response. Immunofluorescence staining demonstrated that ALOX5AP was upregulated in WHO higher-grade gliomas, localizing to M2 macrophages. Glioma tissue microarray confirmed the adverse effect of ALOX5AP in the prognosis of glioma. CONCLUSION: ALOX5AP is highly expressed in M2 macrophages and may act as a potential biomarker for predicting prognosis and immunotherapy response in patients with glioma.

Humans↗
PubMed2026

Loss of tumor-infiltrating lymphocytes and poor response to immunotherapy in IDH GOF mutant melanoma.

Recent innovations in melanoma treatment with immune checkpoint blockade (ICB) have improved overall outcomes for patients; however, over 50% of patients still develop resistance to treatment. These patients either have intrinsic resistance and never respond to therapy or develop acquired resistance months or years into treatment. The mechanisms underlying ICB resistance remain poorly understood. Our data show that patients with isocitrate dehydrogenase gain-of-function (IDH GOF) mutant melanoma have a worse response to anti-PD1 immunotherapy. IDH mutations have been found to be oncogenic and associated with differential methylation in multiple cancers but are not yet characterized in human melanoma. Here, we investigate the clinical, immune, and transcriptional phenotypes of IDH GOF melanomas through analyses of clinical response, single-cell RNA-seq, bulk RNA-seq, and DNA methylation data. Single-cell data analysis showed decreased immune infiltrate and activity in the IDH GOF tumors. Bulk sequencing data demonstrated the association among IDH mutation, immune exclusion, and disruptions in global DNA methylation. The melanoma-derived genomic data presented support previously described resistance mechanisms of IDH mutation in other cancer types and is the first demonstration to our knowledge of the role of IDH GOF in the human melanoma tumor microenvironment.

Humans↗
PubMed2026

Mature Tertiary Lymphoid Structures in Breast Cancers Are Associated With Antitumor Immunity and Better Prognosis.

Tertiary lymphoid structures (TLSs) are immune cells accumulated in nonlymphoid tissues, with an inner core of B cells encompassed by T cells. The aim of this study was to evaluate the clinical importance of mature TLSs in breast cancer, including their association with immunotherapy response and their role in modulating the tumor immune microenvironment. We analyzed histopathological data of 726 consecutive primary breast cancers and transcriptomic data of 824 breast cancer samples from the publicly available The Cancer Genome Atlas database to estimate the clinical and immunological values of mature TLSs in breast cancer. Additionally, we utilized pretreatment transcriptomic data of 69 patients with breast cancer from the publicly available I-SPY2 clinical trial to investigate the relation between TLS-related gene signatures and patient responses to immune checkpoint inhibitors. The existence of mature TLSs was identified in ⁓5.6% (41/726) of all patients with breast cancer (hormone receptor-positive human epidermal growth factor receptor-2 negative (HR+HER2-): 0.92%; triple-negative breast cancer (TNBC): 14.96%; and human epidermal growth factor receptor-2 positive (HER2+): 10.98%) and was independently associated with improved recurrence-free survival after adjusting for subtypes, tumor-infiltrating lymphocyte levels, and tumor stage after the multivariable Cox regression analysis in our patient cohort. Notably, the presence of mature TLSs was related to immune cell infiltration in our breast cancer patient cohort. In line with these findings, TLS-related gene signatures analyzed through transcriptomic data reliably reflected the existence of mature TLSs and were related to better clinical responses to immune checkpoint inhibitors in patients with breast cancer. In conclusion, our findings show that mature TLS formation is linked with immune cell infiltration, contributes to a favorable prognosis, and may function as a potential complementary biomarker for immunotherapy response in breast cancer.

Humans↗
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

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗