Backgrounds: Uterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy. Methods: Consensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prognostic value of the prognostic model was calculated by the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. Differential abundance of immune cell infiltration, expression levels of immunomodulators, the status of tumor mutation burden (TMB), the response to immune checkpoint inhibitors (ICIs), drug sensitivity, and functional enrichment in different risk groups were also explored. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the expression patterns of model IRLs in clinical specimens. Results: All UCEC cases were divided into two clusters (C1 = 454) and (C2 = 57) which had significant differences in prognosis and immune status. Five hub IRLs were selected to develop an IRL prognostic signature (IRLPS) which had value in forecasting the clinical outcome of UCEC patients. Biological processes related to tumor and immune response were screened. Function enrichment algorithm showed tumor signaling pathways (ERBB signaling, TGF-β signaling, and Wnt signaling) were remarkably activated in high-risk group scores. In addition, the high-risk group had a higher infiltration level of M2 macrophages and lower TMB value, suggesting patients with high risk were prone to a immunosuppressive status. Furthermore, we determined several potential molecular drugs for UCEC. Conclusion: We successfully identified a novel molecular subtype and inflammation-related prognostic model for UCEC. Our constructed risk signature can be employed to assess the survival of UCEC patients and offer a valuable reference for clinical treatment regimens.

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most lethal cancer due to insufficient actionable molecules. Radiotherapy (RT) plays a vital role in the treatment of ESCC, while radioresistance is a significant challenge to RT and results in locoregional and distant failure. Methods: Radioresistance is a complex involving confounding factors, and its genetic mechanism is challenging to study. Postoperative recurrence after RT is more likely to be due to genetic causes than recurrence in unoperated patients. Therefore, two independent cohorts of ESCC patients who had received postoperative radiotherapy (PORT) and had opposite prognoses were set up, and whole-exome sequencing (WES) technology was applied. We compared the differences in the mutant spectra between the two groups. Results: The mutation rate was slightly higher in the relapsed group than in the stable group [average mutation rate, 1.15 vs. 0.73 mutations per megabyte (Mb)], while the mutation types and proportions in the two groups were not significantly different. In particular, three mutated genes (TTN, MUC19, and NPIPA5) and two copy number alterations (CNAs) (1q amplification and 14q deletion) were identified to be associated with poor RT prognosis, while MUC4 was a favorable factor. Conclusions: These radioresistance biomarkers may supply insight into predicting the radioresponse. Further, these findings offer the first data on the mutational landscape of ESCC radioresistance.

Background. Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers, worldwide. Considering the role of human papilloma virus (HPV) in tumor development and sensitivity to treatment of HNSCC, we aimed to explore the prognostic classification ability of HPV-related signatures in head and neck cancer. Methods. HPV-related signatures were screened out based on Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. HPV-related signatures with prognostic value were identified through univariate Cox regression analysis and a risk signature was established by least absolute shrinkage and selection operator (LASSO). Further, we developed a nomogram by integrating independent prognostic factors. Results. A total of 55 HPV-associated signatures were differentially expressed and ten of them were associated with prognosis of HNSCC patients. The prognostic signature based on CDKN2A, CELSR3, DMRTA2, SERPINE1, TJP3, FADD, and IGF2BP2 expression was constructed. Univariate and multivariate regression analyses demonstrated that the novel prognostic signature was an independent prognostic factor of HNSCC. The nomogram integrating the prognostic signature and other independent prognostic factors was developed. Conclusion. In summary, the prognostic signature of the HPV-related signatures might serve as an important prognostic biomarker for patients with HNSCC.

Background: The accurate preoperative grading of gliomas is critical for guiding therapeutic strategies and prognostic assessment. This study aimed to develop and validate a robust, interpretable machine learning (ML) model based on multicenter magnetic resonance imaging (MRI) radiomics data for predicting the World Health Organization (WHO) grade in glioma patients. Methods: We collected MRI data from 905 glioma patients diagnosed between 2005–2024 across three independent cohorts. Data from 329 of The Cancer Genome Atlas (TCGA) patients served as training (n=230) and internal testing (n=99) sets, while data from 482 University of California San Francisco (UCSF) patients and 94 Nantong University Affiliated Hospital (NTUA) patients served as external validation sets. Radiomics features were extracted from preoperative T1-weighted contrast-enhanced MRI. Ten ML methods, including extreme gradient boosting (XGBoost), were compared using recursive feature elimination (RFE) with cross-validation. The SHapley Additive exPlanation (SHAP) method was used for the model interpretability analysis. Results: Among the 10 ML models, XGBoost performed best with area under the curve (AUC) values of 0.983 [95% confidence interval (CI): 0.968–0.996] in the training set, 0.897 (95% CI: 0.836–0.956) in the internal testing set, and 0.834 (95% CI: 0.766–0.883) in the UCSF cohort and 0.880 (95% CI: 0.771–0.974) in the NTUA cohort. The model achieved an accuracy of 82.3–94.3%, significantly outperforming conventional imaging assessment. The calibration analysis showed excellent agreement (Hosmer-Lemeshow P>0.05) with a maximum net benefit of 0.42. The SHAP analysis identified 12 optimal features, primarily texture heterogeneity measures from Laplacian of Gaussian (LoG) and wavelet transforms, as key predictors of glioma grade. Conclusions: We successfully developed and validated a robust, interpretable radiomics-based model that accurately predicts glioma WHO grade preoperatively. Its promising performance across diverse datasets suggests potential for clinical translation, though prospective validation in real-world clinical workflows is required to confirm clinical utility and assess impact on treatment decisions and patient outcomes.

Background: Lung adenocarcinoma (LUAD) is responsible for majority cases of lung cancer and considered to be the primary cause of cancer-related mortality. The imbalance of cellular proliferation and apoptosis is critically implicated in the pathogenesis and progression of LUAD. Sphingomyelin, a vital lipid component, is integral to the regulation of tumor cell growth and apoptosis, and has garnered significant attention as a target in novel anticancer therapies. The pivotal molecules involved in sphingomyelin metabolism are crucial in modulating tumor cell behavior, thereby influencing clinical outcomes.Methods: A comprehensive consensus clustering analysis was conducted by collecting clinical LUAD figures from the TCGA and GEO databases. By employing Cox regression and Lasso regression analysis, a prognostic model for LUAD patients was established by identifying seven sphingolipid-related genes (SRGs), and validated in the GEO database. The study also delved into the clinical relevance, functional capabilities, and immune implications of prognostic signals associated with sphingolipid metabolism. Finally, experiments conducted in vitro confirmed the imbalance of sphingolipid-associated genes in LUAD.Results: Using the prognostic model, lung adenocarcinoma (LUAD) patients can be divided into high-risk and low-risk groups. Meanwhile, we can observe marked disparities in survival times among these groups. Additionally, the model demonstrates high predictive accuracy in external validation cohorts. Research on the immune microenvironment and immunotherapy points to this risk stratification as a useful reference for immunotherapeutic strategies in LUAD. Finally, our hypothesis was corroborated through in vitro experiments.Conclusion: This study demonstrates that sphingolipid-related gene prognostic characteristics correlate with tumor progression and recurrence, long-term prognosis, and immune infiltration in LUAD patients. The outcomes of our study could help shape innovative strategies for early intervention and prognosis prediction in lung adenocarcinoma.

Background: Epigenetic reprogramming, particularly DNA methylation mediated by DNA-methyltransferase 1 (DNMT1), plays a crucial role in the pathogenesis of hepatocellular carcinoma (HCC). However, the regulatory mechanisms underlying DNMT1 overexpression and its relationship with immune infiltration in HCC remain unclear. This study aimed to investigate the expression pattern, prognostic significance, and regulatory network of DNMT1 in HCC, as well as its correlation with the tumor immune microenvironment. Methods: A pan-cancer analysis of DNMT1 expression and prognosis was conducted using The Cancer Genome Atlas (TCGA) data. A cohort of 102 pairs of HCC and adjacent normal tissues from patients who underwent surgical resection was analyzed by immunohistochemistry (IHC). Upstream regulatory non-coding RNAs (ncRNAs) were identified by bioinformatics analyses, including an expression correlation analysis, survival analysis, and competing endogenous RNA (ceRNA) network construction. The relationship between DNMT1 expression and immune cell infiltration was evaluated using multiple algorithms, including TIMER, CIBERSORT, and xCell. A Gene Set Enrichment Analysis (GSEA) was performed to identify the DNMT1-associated functional pathways. Results: DNMT1 was significantly overexpressed in the HCC tissues compared to the normal control tissues, and was correlated with poor overall survival (OS) and relapse-free survival (RFS). High DNMT1 expression was associated with advanced histological grade, elevated alpha-fetoprotein (AFP) levels, and older age (P<0.001). SNHG3 was confirmed to be an upstream long non-coding RNA (lncRNA) that competitively binds to hsa-miR-148a-3p, forming the SNHG3/hsa-miR-148a-3p/DNMT1 regulatory axis. DNMT1 expression was significantly positively correlated with the infiltration of multiple immune cell types, including B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells (DCs). Moreover, DNMT1 expression was significantly positively correlated with immune checkpoint molecules, including programmed cell death protein 1 (PD-1), programmed death ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4). The GSEA results revealed that high DNMT1 expression was enriched in epithelial-mesenchymal transition (EMT), E2F targets, the G2M checkpoint, and inflammatory response pathways. Conclusions: The SNHG3/miR-148a-3p/DNMT1 axis represents a novel regulatory pathway in hepatocarcinogenesis. DNMT1 overexpression could serve as a biomarker for poor prognosis, and is closely associated with enhanced immune cell infiltration and immune checkpoint expression in HCC. These findings suggest that DNMT1 may serve as a potential therapeutic target for both conventional treatment and immunotherapy in HCC patients.