Apoptosis-Related Gene-Mediated Cell Death Pattern Induces Immunosuppression and Immunotherapy Resistance in Gastric Cancer

Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, and DNA methylation data, from the sxdyc website. We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index. The prognostic relationship between CMLS and STAD was assessed using Kaplan-Meier curves, and the independent prognostic value of CMLS was determined by univariate and multivariate regression analyses. we also evaluated the immune characteristics, immunotherapy response, and drug sensitivity of different CMLS groups. The results of the multiomics analysis classified STAD into three subtypes, with CS1 resulting in the best survival outcome. In total, 10 hub genes (CES3, AHCYL2, APOD, EFEMP1, CYP1B1, ASPN, CPE, CLIP3, MAP1B, and DKK1) were screened and constructed the CMLS was significantly correlated with prognosis in patients with STAD and was an independent prognostic factor for patients with STAD. Using the CMLS risk score, all patients were divided into a high CMLS group and a low CMLS group. Patients in the low-CMLS group had better survival, more enriched immune cells, and higher tumor mutation load scores, suggesting better immunotherapy responsiveness and a possible "hot tumor" phenotype. Patients in the high-CMLS group had a significantly poorer prognosis and were less sensitive to immunotherapy but were likely to benefit more from chemotherapy and targeted therapy. In this study, 10 clustering methods and 10 machine learning methods were combined to analyze the multiomics of STAD, classify STAD into three subtypes, and constructed CMLS-related prognostic model features, which are important for accurate management and effective treatment of STAD.

Identification of biomarkers related to Escherichia coli infection for the diagnosis of gastrointestinal tumors applying machine learning methods

Background

Escherichia coli (E. coli) is a part of normal gastrointestinal microbiota but it could also cause human gastrointestinal diseases. Understanding the mechanism of E. coli in the progression of gastrointestinal tumors can provide novel prevention and treatment strategies for gastrointestinal tumors.

Methods

The E. coli infection score was calculated by single sample GSEA (ssGSEA). Weighted correlation network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify genes related to E. coli infection in gastrointestinal tumors. Hub genes were selected by machine learning methods to establish a diagnostic model. The diagnostic performance of the model was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and validated in three external datasets. After determining the biomarkers, immune infiltration analysis and GSEA were further performed. The mRNA expressions of the biomarkers in stomach adenocarcinoma (STAD) cells and the invasion and migration of the tumor cells were detected by conducting in vitro experiments.

Results

The E. coli infection score was lower in tumor samples than in normal samples. Eight hub genes were selected from a total of 28 genes associated with E. coli-related dysbiosis in gastrointestinal tumors to establish an accurate diagnostic model. The AUC values of PRKCB and IL16 were all greater than 0.7 in three external datasets and the mRNA expression pattern was consistent with TCGA cohort, therefore PRKCB and IL16 were selected as the diagnostic biomarkers. PRKCB and IL16 exhibited significant positive correlations with most immune cells, and inflammation-related pathways were activated in the high expression groups of PRKCB and IL16. Moreover, IL16 was high-expressed but PRKCB was low-expressed in STAD cells, and silencing IL16 suppressed the invasion and migration of STAD cells.

Conclusions

Overall, we identified and validated 8 robust genes related to E. coli applying bioinformatics and machine learning algorithms, providing theoretical foundations for the relationship between E. coli-related dysbiosis and gastrointestinal tumors.

RNA processing modification mediated subtypes illustrate the distinctive features of tumor microenvironment in hepatocellular carcinoma

Multiple transcript isoforms of genes can be formed by processing and modifying the 5' and 3' ends of RNA. Herein, the aim of this study is to uncover the characteristics of RNA processing modification (RPM) in hepatocellular carcinoma (HCC), and to identify novel biomarkers and potential targets for treatment. Firstly, integrated bioinformatics analysis was carried out to identify risk prognostic RPM regulators (RPMRs). Then, we used these RPMRs to identify subtypes of HCC and explore differences in immune microenvironment and cellular function improvement pathways between the sub-types. Finally, we used the principal component analysis algorithms to estimate RPMscore, which were applied to 5 cohorts. Lower RPMscore among patients correlated with a declined survival rate, increased immune infiltration, and raised expression of immune checkpoints, aligning with the "immunity tidal model theory". The RPMscore exhibited robust, which was validated in multiple datasets. Mechanistically, low RPMscore can create an immunosuppressive microenvironment in HCC by manipulating tumor-associated macrophages. Preclinically, patients with high RPMscore might benefit from immunotherapy. The RPMscore is helpful in clustering HCC patients with distinct prognosis and immunotherapy. Our RPMscore model can help clinicians to select personalized therapy for HCC patients, and RPMscore may act a part in the development of HCC.

Leveraging Diverse Regulated Cell Death Patterns to Identify Diagnosis Biomarkers for Alzheimer's Disease

BACKGROUND

The functions of regulated cell death (RCD) are closely related to Alzheimer's disease (AD). However, very few studies have systematically investigated the diagnosis and immunologic role of RCD-related genes in AD patients.

METHODS

8 multicenter AD cohorts were included in this study, and then were merged into a meta cohort. Then, an unsupervised clustering analysis was carried out to detect unique subtypes of AD based on RCD-related genes. Subsequently, differently expressed genes (DEGs) and weighted correlation network analysis (WGCNA) between subtypes were identified. Finally, to establish an optimal risk model, an RCD.score was constructed by using computational algorithm (10 machine-learning algorithms, 113 combinations).

RESULTS

We identified two distinct subtypes based on RCD-related genes, each exhibiting distinct hallmark pathway activity and immunologic landscape. Specifically, cluster.A patients had a higher immune infiltration, a higher immune modulators and poor AD progression. Utilizing the shared DEGs and WGCNA of these subtypes, we constructed an RCD.score that demonstrated excellent predictive ability in AD across multiple datasets. Furthermore, RCD.score was identified to exhibit the strongest association with poor AD progression. Mechanistically, we observed activation of signaling pathways and effective immune infiltration and immune modulators in the high RCD.score group, thus leading to a poor AD progression. Additionally, Mendelian randomization screening revealed four genes (CXCL1, ENTPD2, METTL7A, and SERPINB6) as feature genes for AD.

CONCLUSION

The RCD model is a valuable tool in categorizing AD patients. This model can be of great assistance to clinicians in determining the most suitable personalized treatment plan for each individual AD patient.

Apoptosis related genes mediated molecular subtypes depict the hallmarks of the tumor microenvironment and guide immunotherapy in bladder cancer

Apoptosis has been discovered as a mechanism of cell death. The purpose of this study is to identify the diagnostic signature factors related to bladder cancer (BLCA) through apoptosis related genes (ARGs). Clinicopathological parameters and transcriptomics data of 1,440 BLCA patients were obtained from 7 datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, TCGA-BLCA, and IMvigor210). We first identified prognosis-related ARGs in BLCA and used them to construct two ARGs molecular subtypes by using consensus clustering algorithm. By using principal component analysis algorithms, a ARGscore was constructed to quantify the index of individualized patient. High ARGscore correlated with progressive malignancy and poor outcomes in BLCA patients. High ARGscore was associated with higher immune cell, higher estimate scores, higher stromal scores, higher immune scores, higher immune checkpoint, and lower tumor purity, which was consistent with the "immunity tidal model theory". Preclinically, BLCA immunotherapy cohorts confirmed patients with low ARGscore demonstrated significant therapeutic advantages and clinical benefits. These findings contribute to our understanding of ARGs and immunotherapy in BLCA. The ARGscore is a potentially useful tool to predict the prognosis and immunotherapy in BLCA.