Pulmonary metastasis remains the leading cause of mortality in patients with osteosarcoma (OS). Anoikis functions as a physiological barrier to metastasis by eliminating detached cells. Tumor cells with high anoikis resistance can survive detachment and circulation, facilitating pulmonary metastasis. However, the prognostic significance and biological role of anoikis-related genes (ARGs) in osteosarcoma remain largely unexplored. Transcriptomic profiles and clinical information of osteosarcoma patients were obtained from the TCGA database (n = 90) and used as the training cohort. Anoikis-related genes were retrieved from the GeneCards database. Differential expression analysis was performed between metastatic and non-metastatic osteosarcoma samples, and candidate ARGs were identified by intersecting differentially expressed genes with ARGs. A prognostic signature was established using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox analysis. Kaplan–Meier survival analysis, receiver operating characteristic (ROC) curves, multivariate Cox regression, and nomogram analysis were performed to evaluate the predictive performance of the model. Two independent GEO datasets (GSE87624, n = 52 ; GSE220538, n = 38) were used for external validation. Tumor microenvironment characteristics were assessed using immune cell deconvolution with the immunedeconv package and immune infiltration analyses. Furthermore, single-cell RNA sequencing data (GSE152048, n = 11; GSE250015, n = 2) were analyzed to validate the cellular distribution and biological relevance of the risk signature. A total of seven metastasis-associated ARGs were identified, among which five genes (UCHL1, ST3GAL1, NTF3, NGFR, and CRABP2) were incorporated into the final anoikis-resistance related gene signature(AR-Score). Patients in the high-risk group exhibited significantly worse metastasis-free survival (MFS) than those in the low-risk group (P < 0.001). Multivariate Cox regression demonstrated that the risk score was an independent prognostic factor for osteosarcoma (HR = 6.53, 95
Bladder cancer (BC) presents a significant clinical challenge, with a poor prognosis in its advanced stages. Although immunotherapy has improved outcomes, patient response rates remain limited due to complex immune evasion mechanisms. Robust biomarkers are urgently needed for prognostic stratification and to help identify patients who might benefit from immunotherapy. We developed an immune evasion-related signature (IRS) using transcriptomic data from The Cancer Genome Atlas (TCGA) and validated it across four independent Gene Expression Omnibus (GEO) cohorts. An integrative machine learning framework based on ten algorithms, fitted as 60 candidate model configurations, was used to construct the IRS from differentially expressed immune-related genes. The signature’s prognostic value, association with the tumor immune microenvironment (TME), and predictive power for immunotherapy response were assessed, and the key gene DOT1L was functionally validated in vitro. The IRS, comprising five genes (DOT1L, DVAC2, SRRT, PPP2R3C, and IRF9), effectively stratified BC patients into high-risk and low-risk groups. A high IRS score was an independent prognostic factor for poorer overall survival and was associated with an immunosuppressive TME, characterized by reduced CD8+ T cell infiltration and higher Tumor Immune Dysfunction and Exclusion (TIDE) scores. Conversely, low IRS scores correlated with enhanced response to anti-PD-1/L1 therapy in validation cohorts. Furthermore, DOT1L knockdown suppressed BC cell proliferation and migration. We developed and retrospectively validated a machine learning-derived IRS that showed consistent associations with prognosis and immunotherapy benefit across multiple independent bladder cancer cohorts. The IRS is a promising research biomarker, and prospective, multicenter validation is required before it can guide personalized treatment decisions.
Cervical cancer is the fourth most prevalent cancer among women globally, with a high incidence in low- and middle-income countries. Despite advances in screening, the increasing morbidity and mortality rates underscore the need for novel diagnostic and prognostic biomarkers. Cisplatin is a cornerstone of cervical cancer treatment, but resistance to cisplatin remains a significant challenge. This study investigates the role of disulfidptosis-related genes (DRGs) in cervical cancer prognosis and response to cisplatin chemotherapy, aiming to identify potential biomarkers to inform treatment strategies. Bioinformatics approaches were used to identify DRGs associated with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) using data from The Cancer Genome Atlas (TCGA) and the GTEx Genotype-Tissue Expression (GTEx) databases. Gene expression and methylation levels of 24 DRGs were analyzed in cervical cancer and normal tissues, and their correlation with survival outcomes in cisplatin-treated patients was examined. Immune infiltration analysis was performed using the TIMER Tumor Immune Estimation Resource (TIMER) 2.0 platform. Enrichment analysis was conducted using the STRING and Metascape databases to explore the biological relevance of DRGs in cervical cancer. The expression analysis revealed significant differential expression of DRGs in cervical cancer, with RPN1 and CD2AP overexpressed, and FLNA, TLN1, and MYH10 underexpressed compared to normal tissues. Notably, methylation analysis indicated lower methylation of MYH10 and CD2AP in cervical cancer, suggesting their potential as early biomarkers. High expression of TLN1 was associated with poorer survival outcomes in cisplatin-treated patients (time-dependent Area Under the Curve = 0.658 at 3 years). Furthermore, drug sensitivity analysis using the Genomics of Drug Sensitivity in Cancer (GDSC) database showed a positive correlation trend between TLN1 expression and cisplatin Half-Maximal Inhibitory Concentration (IC50) in cervical cancer cell lines (Pearson’s r = 0.488, p = 0.090). Immune infiltration analysis showed a correlation between TLN1 expression and the presence of B cells, CD4 + T cells, neutrophils, and dendritic cells in the tumor microenvironment, which may impact tumor progression. Additionally, TLN1 was linked to key pathways involved in adhesion, cytoskeletal regulation, and immune response. This study highlights the importance of DRGs in cervical cancer prognosis and cisplatin chemotherapy response. Our findings suggest that high TLN1 expression may serve as a potential prognostic biomarker for poor prognosis in cervical cancer patients treated with cisplatin, potentially linked to cytoskeletal regulation and immune cell infiltration. These hypothesis-generating results warrant further investigation in larger independent cohorts to validate the prognostic value of TLN1 and explore its functional relevance to disulfidptosis. These results offer potential insights for enhancing treatment strategies and warrant further investigation into the functional roles of DRGs in cervical cancer.
N6-methyladenosine (m6A) is a reversible and context-dependent RNA modification that links transcript processing to bladder cancer growth, immune escape, treatment response, and tumor-cell plasticity. This review asks three questions: which m6A mechanisms have been experimentally resolved in bladder cancer; how CRISPR systems can establish causal relationships at regulator, transcript, and nucleotide levels; and which targets are sufficiently supported for translational development. We report the experimental settings represented for each axis and assess mechanistic resolution separately; cross-cancer observations are retained only as hypotheses for bladder cancer. Bladder-cancer evidence identifies oncogenic METTL3-centered axes involving AFF4-NF-kB-MYC, ITGA6, pri-miR-221/222-PTEN, RPN2, and PD-L1; an FTO-MALAT1-miR-384-MAL2 axis; YTHDF2-dependent degradation of DDX58/RIG-I; WTAP-NRF2 regulation; and IGF2BP3-mediated stabilization of HMGB1. Reader-resolved axes include METTL3-ITGA6-YTHDF1/3, WTAP-NRF2-YTHDF1, FTO-MALAT1-YTHDF2, YTHDF2-DDX58, IGF2BP3-HSP90AB1, and IGF2BP3-CDK6; METTL3-RPN2-YTHDF1 and METTL3-PD-L1-IGF2BP1 remain reader-implicated because direct reader-target binding was not demonstrated, and RPN2 additionally lacks experimental target-specific m6A mapping. IGF2BP3-HMGB1, YTHDC1-HECW1 and IGF2BP3-PPARγ also remain reader-implicated, with m6A dependence unestablished for HMGB1 and HECW1. No axis has achieved site-resolved validation with site-mutant phenotypic rescue in an immune-competent orthotopic model. METTL14 findings remain directionally inconsistent. Cas9/Cas12a DNA editing, active Cas13 RNA knockdown, and inactive RNA-targeting Cas platforms carrying m6A effectors are treated as distinct technologies. Two bladder-cancer proof-of-concept studies used guide-directed m6A editing: RCas9-METTL3 targeted CDCP1, and a multisite dCasRx-METTL3/ALKBH5 platform targeted ITGA6. These experiments support causal research applications but do not constitute an established therapy. We propose a controlled workflow integrating m6A mapping, genetic perturbation, site-mutant rescue, reader identification, organoids, in vivo models, and therapy-response assays. Detection, biomarker development, pharmacology, delivery, and safety are evaluated with emphasis on analytical validity and the absence of clinical validation for m6A-directed treatment in bladder cancer.
Osteosarcoma (OS) is a primary bone cancer affecting adolescents and young adults, with high mortality and recurrence rates. This study integrated publicly available transcriptomic, clinical, and somatic mutation data to derive an exploratory prognostic risk signature. A broad pool of 3,745 analyzable candidate genes was obtained by matching genes collected through mitophagy- and tumor metabolism-associated searches of GeneCards and relevant literature to an OS expression matrix. Exploratory differential expression analysis identified 25 candidate genes, including 15 upregulated and 10 downregulated genes. Weighted gene co-expression network analysis (WGCNA) narrowed these to 12 module genes. Survival modeling in the TARGET-OS discovery cohort, including least absolute shrinkage and selection operator (LASSO) regression, subsequently identified a five-gene signature comprising BBS4, CTNND2, EPHA3, MAPK10, and MYC. These genes represent statistically prioritized candidate prognostic biomarkers; their direct functional contributions to mitophagy or tumor metabolism in OS were not established. The signature stratified overall survival in the TARGET-OS discovery cohort (p < 0.001), but significant survival stratification was not reproduced in GSE16091, GSE21257, or GSE39055 (p = 0.35, 0.19, and 0.61, respectively). The model genes showed risk-group-specific correlations with computationally estimated immune-cell features. Single-cell analysis described their cell-type-specific expression, while drug-sensitivity analysis identified differences in computationally predicted drug responses. These findings provide hypotheses for further investigation rather than evidence of direct immune regulation or clinical treatment selection. The five-gene signature therefore remains an exploratory finding requiring independent prognostic and functional validation.
