The comprehensive and systematic identification of BLCA-specific SF-regulated, survival-related AS events

Uncovering the ceRNA Network Related to the Prognosis of Stomach Adenocarcinoma Among 898 Patient Samples

Stomach adenocarcinoma (STAD) patients are often associated with significantly high mortality rates and poor prognoses worldwide. Among STAD patients, competing endogenous RNAs (ceRNAs) play key roles in regulating one another at the post-transcriptional stage by competing for shared miRNAs. In this study, we aimed to elucidate the roles of lncRNAs in the ceRNA network of STAD, uncovering the molecular biomarkers for target therapy and prognosis. Specifically, a multitude of differentially expressed lncRNAs, miRNAs, and mRNAs (i.e., 898 samples in total) was collected and processed from TCGA. Cytoplasmic lncRNAs were kept for evaluating overall survival (OS) time and constructing the ceRNA network. Differentially expressed mRNAs in the ceRNA network were also investigated for functional and pathological insights. Interestingly, we identified one ceRNA network including 13 lncRNAs, 25 miRNAs, and 9 mRNAs. Among them, 13 RNAs were found related to the patient survival time; their individual risk score can be adopted for prognosis inference. Finally, we constructed a comprehensive ceRNA regulatory network for STAD and developed our own risk-scoring system that can predict the OS time of STAD patients by taking into account the above.

Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer

Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key roles in its prognosis. In particular, immune-related lncRNAs (IRL) have attracted enormous interest in diagnosis and treatment, but less is known about their potential functions. We aimed to investigate dysfunctional IRL and construct a risk model for improving the outcomes of patients. Nineteen immune cell types were collected for identifying house-keeping lncRNAs (HKLncRNA). GSE39582 and TCGA-COAD were treated as the discovery and validation datasets, respectively. Four machine learning algorithms (LASSO, Random Forest, Boruta, and Xgboost) and a Gaussian mixture model were utilized to mine the optimal combination of lncRNAs. Univariate and multivariate Cox regression was utilized to construct the risk score model. We distinguished the functional difference in an immune perspective between low- and high-risk cohorts calculated by this scoring system. Finally, we provided a nomogram. By leveraging the microarray, sequencing, and clinical data for immune cells and colon cancer patients, we identified the 221 HKLncRNAs with a low cell type-specificity index. Eighty-seven lncRNAs were up-regulated in the immune compared to cancer cells. Twelve lncRNAs were beneficial in improving performance. A risk score model with three lncRNAs (CYB561D2, LINC00638, and DANCR) was proposed with robust ROC performance on an independent dataset. According to immune-related analysis, the risk score is strongly associated with the tumor immune microenvironment. Our results emphasized IRL has the potential to be a powerful and effective therapy for enhancing the prognostic of colon cancer.

Alternative splicing of PSMD13 mediated by genetic variants is significantly associated with endometrial cancer risk

OBJECTIVE

Accumulating evidence has shown that aberrant alternative splicing events are closely associated with the onset and development of cancer. However, whether genetic variants-associated alternative splicing is linked to risk of endometrial cancer remains largely uncertain.

METHODS

We identified single nucleotide polymorphisms (SNPs) locates in the splicing number trait locus (sQTL) of endometrial cancer using the CancerSplicing QTL database. In parallel with bioinformatics analysis, we conducted a case-control study comprising 2,000 cases and 2,013 controls to assess the association between identified SNP which possesses mRNA splicing function and endometrial cancer susceptibility. Furthermore, we used the Kaplan-Meier Plotter, The Human Protein Atlas, SPNR, and Spliceman2 databases for sQTL and differential gene expression analyses to identify the genetic variant which most potentially influence the risk of endometrial cancer through alternative splicing to reveal the potential mechanism by which candidate SNPs regulate the risk of endometrial cancer.

RESULTS

The results indicated that SNP rs7128029 A

CONCLUSION

These findings suggest that SNP rs7128029-mediated alternative splicing events in PSMD13 are associated with endometrial cancer risk and may be a potential early screening biomarker for endometrial cancer-susceptible populations.

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Key Words

• 26S Proteasome Non-ATPase Regulatory Subunit 13
• Alternative Splicing
• Endometrial Cancer
• Single Nucleotide Polymorphism

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MESH Headings

• Female
• Humans
• Alternative Splicing/genetics
• Case-Control Studies
• Databases, Factual
• Endometrial Neoplasms/genetics
• Odds Ratio
• Risk
• Polymorphism, Single Nucleotide

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Grants

• (2022)626 Natural Science Foundation of Guangzhou City
• (2022)402 Zunyi City Science and Technology Plan Project
• 2018 F-913 Zunyi Medical University

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Affiliation(s)

Sisi He Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
Rong Cao The Second Clinical College, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
Yan Mao Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
Na Li Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
Yanzhe Wang Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
Hu Ma Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
Kunming Tian Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China Department of Preventive Medicine, School of Public Health, Zunyi Medical University, Zunyi, China.

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Alternative splicing in bladder cancer: potential strategies for cancer diagnosis, prognosis, and treatment

Bladder cancer is the most common malignancy of the urinary tract worldwide. The therapeutic options to tackle this disease comprise surgery, intravesical or systemic chemotherapy, and immunotherapy. Unfortunately, a wide number of patients ultimately become resistant to these treatments and develop aggressive metastatic disease, presenting a poor prognosis. Therefore, the identification of novel therapeutic approaches to tackle this devastating pathology is urgently needed. However, a significant limitation is that the progression and drug response of bladder cancer is strongly associated with its intrinsic molecular heterogeneity. In this sense, RNA splicing is recently gaining importance as a critical hallmark of cancer since can have a significant clinical value. In fact, a profound dysregulation of the splicing process has been reported in bladder cancer, especially in the expression of certain key splicing variants and circular RNAs with a potential clinical value as diagnostic/prognostic biomarkers or therapeutic targets in this pathology. Indeed, some authors have already evidenced a profound antitumor effect by targeting some splicing factors (e.g., PTBP1), mRNA splicing variants (e.g., PKM2, HYAL4-v1), and circular RNAs (e.g., circITCH, circMYLK), which illustrates new possibilities to significantly improve the management of this pathology. This review represents the first detailed overview of the splicing process and its alterations in bladder cancer, and highlights opportunities for the development of novel diagnostic/prognostic biomarkers and their clinical potential for the treatment of this devastating cancer type. This article is categorized under: RNA Processing > Splicing Regulation/Alternative Splicing RNA in Disease and Development > RNA in Disease.