Development of an m6A subtype classifier to guide precision therapy for patients with bladder cancer.

Purpose: Bladder cancer (BLCA) is a highly heterogeneous tumor. We aim to construct a classifier from the perspective of N6-methyladenosine methylation (m6A) to identify patients with different prognostic risks and treatment responsiveness for precision therapy. Methods: Data on gene expression profile, mutation, and clinical characteristics were mainly obtained from the TCGA-BLCA cohort. Unsupervised clustering was performed to construct m6A subtypes. The tumor microenvironment (TME) landscapes were explored by using ssGSEA, ESTIMATE, and MCPcounter algorithms. K-M survival curves and Cox regression analysis were used to demonstrate the significance of m6A subtypes in predicting prognosis. pRRophetic, oncoPredict, and TIDE algorithms were used to evaluate responsiveness to antitumor therapy. A classifier of m6a subtypes was finally developed based on random forest and artificial neural network (ANN). Results: The two m6A subtypes have significantly different m6A-related gene expression profiles and mutational landscapes. TME analysis showed a higher level of stromal and Inhibitory immune components in subtype B compared with subtype A. The m6A subtype is a clinically independent prognostic predictor of BLCA, subtype B has a poorer prognosis. Drug sensitivity analysis showed that subtype B has lower IC50 values and AUC values for cisplatin and docetaxel. Efficacy assessment showed significantly poorer radiotherapy efficacy and lower immunotherapy responsiveness in subtype B. We finally constructed an ANN classifier to accurately classify BLCA patients into two m6A subtypes. Conclusion: Our study developed a classifier for identifying subtypes with different m6A characteristics, and BLCA patients with different m6A subtypes have significantly different prognosis and responsiveness to antitumor therapy.

Multi-omics reveals the impact of cancer-associated fibroblasts on the prognosis and treatment response of adult diffuse highest-grade gliomas.

Background: Cancer associated fibroblasts (CAF), an important cancer-promoting and immunosuppressive component of the tumor immune microenvironment (TIME), have recently been found to infiltrate adult diffuse highest-grade gliomas (ADHGG) (gliomas of grade IV). Methods: Gene expression and clinical data of ADHGG patients were obtained from the CGGA and TCGA databases. Consensus clustering was used to identify CAF subtypes based on CAF key genes acquired from single-cell omics and spatial transcriptomomics. CIBERSORT, ssGSEA, MCPcounter, and ESTIMATE analyses were used to assess the TIME of GBM. Survival analysis, drug sensitivity analysis, TCIA database, TIDE and cMap algorithms were used to compare the prognosis and treatment response between patients with different CAF subtypes. An artificial neural network (ANN) model based on random forest was constructed to exactly identify CAF subtypes, which was validated in a real-world patient cohort of ADHGG. Results: Consensus clustering classified ADHGG into two CAF subtypes. Compared with subtype B, patients with ADHGG subtype A had a poorer prognosis, worse responsiveness to immunotherapy and radiotherapy, higher CAF infiltration in TIME, but higher sensitivity to temozolomide. Furthermore, patients with subtype A had a much lower proportion of IDH mutations. Finally, the ANN model based on five genes (COL3A1, COL1A2, CD248, FN1, and COL1A1) could exactly discriminate CAF subtypes, and the validation of the real-world cohort indicated consistent results with the bioinformatics analyses. Conclusion: This study revealed a novel CAF subtype to distinguish ADHGG patients with different prognosis and treatment responsiveness, which may be helpful for accurate clinical decision-making of ADHGG.

Identifying disulfidptosis subtypes in hepatocellular carcinoma through machine learning and preliminary exploration of its connection with immunotherapy.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly prevalent and deadly cancer, with limited treatment options for advanced-stage patients. Disulfidptosis is a recently identified mechanism of programmed cell death that occurs in SLC7A11 high-expressing cells due to glucose starvation-induced disintegration of the cellular disulfide skeleton. We aimed to explore the potential of disulfidptosis, as a prognostic and therapeutic marker in HCC. METHODS: We classified HCC patients into two disulfidptosis subtypes (C1 and C2) based on the transcriptional profiles of 31 disulfrgs using a non-negative matrix factorization (NMF) algorithm. Further, five genes (NEIL3, MMP1, STC2, ADH4 and CFHR3) were screened by Cox regression analysis and machine learning algorithm to construct a disulfidptosis scoring system (disulfS). Cell proliferation assay, F-actin staining and PBMC co-culture model were used to validate that disulfidptosis occurs in HCC and correlates with immunotherapy response. RESULTS: Our results suggests that the low disulfidptosis subtype (C2) demonstrated better overall survival (OS) and progression-free survival (PFS) prognosis, along with lower levels of immunosuppressive cell infiltration and activation of the glycine/serine/threonine metabolic pathway. Additionally, the low disulfidptosis group showed better responses to immunotherapy and potential antagonism with sorafenib treatment. As a total survival risk factor, disulfS demonstrated high predictive efficacy in multiple validation cohorts. We demonstrated the presence of disulfidptosis in HCC cells and its possible relevance to immunotherapeutic sensitization. CONCLUSION: The present study indicates that novel biomarkers related to disulfidptosis may serve as useful clinical diagnostic indicators for liver cancer, enabling the prediction of prognosis and identification of potential treatment targets.

Construction of the novel immune risk scoring system related to CD8+ T cells in uterine corpus endometrial carcinoma.

BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with high incidence and poor prognosis. Although immunotherapy has brought significant survival benefits to advanced UCEC patients, traditional evaluation indicators cannot accurately identify all potential beneficiaries of immunotherapy. Consequently, it is necessary to construct a new scoring system to predict patient prognosis and responsiveness of immunotherapy. METHODS: CIBERSORT combined with weighted gene co-expression network analysis (WGCNA), non-negative matrix factorization (NMF), and random forest algorithms to screen the module associated with CD8+ T cells, and key genes related to prognosis were selected out by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to develop the novel immune risk score (NIRS). Kaplan-Meier (K-M) analysis was used to compare the difference of survival between high- and low- NIRS groups. We  also explored the correlations between NIRS, immune infiltration and immunotherapy, and three external validation sets were used to verify the predictive performance of NIRS. Furthermore, clinical subgroup analysis, mutation analysis, differential expression of immune checkpoints, and drug sensitivity analysis were performed to generate individualized treatments for patients with different risk scores. Finally, gene set variation analysis (GSVA) was conducted to explore the biological functions of NIRS, and qRT-PCR was applied to verify the differential expressions of three trait genes at cellular and tissue levels. RESULTS: Among the modules clustered by WGCNA, the magenta module was most positively associated with CD8+ T cells. Three genes (CTSW, CD3D and CD48) were selected to construct NIRS after multiple screening procedures. NIRS was confirmed as an independent prognostic factor of UCEC, and patients with high NIRS had significantly worse prognosis compared to those with low NIRS. The high NIRS group showed lower levels of infiltrated immune cells, gene mutations, and expression of multiple immune checkpoints, indicating reduced sensitivity to immunotherapy. Three module genes were identified as protective factors positively correlated with the level of CD8+ T cells. CONCLUSIONS: In this study, we constructed NIRS as a novel predictive signature of UCEC. NIRS not only differentiates patients with distinct prognoses and immune responsiveness, but also guides their therapeutic regimens.

Hepatic Abscess Secondary to Stomach Perforation by a Fish Bone: a Rare Cause of Hepatic Abscess.

Gastrointestinal foreign bodies are commonly encountered in clinical practice. However, although perforation of the gastrointestinal tract by a foreign body is not unusual, the formation of a hepatic abscess as a result of the migration of a foreign body is extremely rare. Patients usually present with atypical symptoms, and the treatment of such pyogenic liver abscesses presents a challenge. Here we report a case of hepatic abscess secondary to stomach perforation by a fish bone.