Prognostic value of RNA methylation-related genes in gastric adenocarcinoma based on bioinformatics.

Background: Gastric cancer (GC) is a malignant tumor that originates from the epithelium of the gastric mucosa and has a poor prognosis. Stomach adenocarcinoma (STAD) covers 95% of total gastric cancer. This study aimed to identify the prognostic value of RNA methylation-related genes in gastric cancer. Methods: In this study, The Cancer Genome Atlas (TCGA)-STAD and GSE84426 cohorts were downloaded from public databases. Patients were classified by consistent cluster analysis based on prognosis-related differentially expressed RNA methylation genes Prognostic genes were obtained by differential expression, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. The prognostic model was established and validated in the training set, test set and validation set respectively. Independent prognostic analysis was implemented. Finally, the expression of prognostic genes was affirmed by reverse transcription quantitative PCR (RT-qPCR). Results: In total, four prognostic genes (ACTA2, SAPCD2, PDK4 and APOD) related to RNA methylation were identified and enrolled into the risk signature. The STAD patients were divided into high- and low-risk groups based on the medium value of the risk score, and patients in the high-risk group had a poor prognosis. In addition, the RNA methylation-relevant risk signature was validated in the test and validation sets, and was authenticated as a reliable independent prognostic predictor. The nomogram was constructed based on the independent predictors to predict the 1/3/5-year survival probability of STAD patients. The gene set enrichment analysis (GSEA) result suggested that the poor prognosis in the high-risk subgroup may be related to immune-related pathways. Finally, the experimental results indicated that the expression trends of RNA methylation-relevant prognostic genes in gastric cancer cells were in agreement with the result of bioinformatics. Conclusion: Our study established a novel RNA methylation-related risk signature for STAD, which was of considerable significance for improving prognosis of STAD patients and offering theoretical support for clinical therapy.

CircFBXW4 Suppresses Colorectal Cancer Progression by Regulating the MiR-338-5p/SLC5A7 Axis.

Dysregulated circular RNAs (circRNAs) contribute to tumourigenesis and cancer progression. However, the expression patterns and biological functions of circRNAs in colorectal cancer (CRC) remain elusive. Here, RNA sequencing and bioinformatics analyses are applied to screen for aberrantly expressed circRNAs. The expression of circFBXW4 in CRC tissues and cell lines is determined by quantitative real-time PCR. A series of in vitro and in vivo biological function assays are implemented to assess the functions of circFBXW4. The regulatory mechanisms linking circFBXW4, miR-338-5p, and SLC5A7 are explored by western blotting, dual luciferase reporter assays, and RNA pull-down assays. CircFBXW4 is dramatically downregulated in CRC tissues and cell lines. circFBXW4 downregulation is clearly correlated with malignant features and patient overall survival in CRC. Functionally, ectopic expression of circFBXW4 strikingly impairs the proliferation, migration, and invasion capacities of CRC cells in vitro and in vivo, whereas circFBXW4 knockdown has the opposite effects. Mechanistically, circFBXW4 competitively binds to miR-338-5p and prevents it from interacting with and repressing its target SLC5A7, thus suppressing the progression of CRC. This study reveals the specific critical role of circFBXW4 in inhibiting CRC progression via the miR-338-5p/SLC5A7 axis and provides an additional target for eradicating CRC.

Using association mapping and local interval haplotype association analysis to improve the cotton drought stress response.

Drought stress has a serious impact on the growth and development of cotton. To explore the relevant molecular mechanism of the drought stress response in cotton, gene mapping based on the QTL interval mapped by simplified genome BSA-seq of the drought-resistance-related RIL population was performed. A QTL region spanning 2.02 Mb on chromosome D07 was selected, and 201 resource materials were genotyped using 9 KASP markers in the interval. After local interval haplotype association analysis, the overlap of the 110 kb peak region confirmed the reliability of this region, and at the same time, the role of GhGF14-30, the only gene in the overlapping region, was modeled in the response of cotton to drought stress. qRTPCR analysis of the materials and population parents proved that this gene plays a role in the drought stress response in cotton. Virus-induced gene silencing proved the importance of this gene in drought-sensitive materials, and drought-resistance-related marker genes also proved that the GhGF14-30 gene may play an important role in the ABA and SOS signaling pathways. This study provides a basis for mining drought stress response functional genes in cotton and lays the foundation for the molecular mechanism of the GhGF14-30 gene in response to drought stress in cotton.

Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories.

Research on the heterogeneity of colon cancer (CC) cells is limited. This study aimed to explore the CC cell differentiation trajectory and its clinical implication and to construct a prognostic risk scoring (RS) signature based on CC differentiation-related genes (CDRGs). Cell trajectory analysis was conducted on the GSE148345 dataset, and CDRG-based molecular subtypes were identified from the GSE39582 dataset. A CDRG-based prognostic RS signature was constructed using The Cancer Genome Atlas as the training set and GSE39582 as the validation set. Two subsets with distinct differentiation states, involving 40 hub CDRGs regulated by YY1 and EGR2, were identified by single-cell RNA sequencing data, of which subset I was related to hypoxia, metabolic disorders, and inflammation, and subset II was associated with immune responses and ferroptosis. The CDRG-based molecular subtypes could successfully predict the clinical outcomes of the patients, the tumor microenvironment status, the immune infiltration status, and the potential response to immunotherapy and chemotherapy. A nomogram integrating a five-CDRG-based RS signature and prognostic clinicopathological characteristics could successfully predict overall survival, with strong predictive performance and high accuracy. The study emphasizes the relevance of CC cell differentiation for predicting the prognosis and therapeutic response of patients to immunotherapy and chemotherapy and proposes a promising direction for CC treatment and clinical decision-making.

Identification of stem cell-related subtypes and risk scoring for gastric cancer based on stem genomic profiling.

BACKGROUND: Although numerous studies demonstrate the role of cancer stem cells in occurrence, recurrence, and distant metastases in gastric cancer (GC), little is known about the evolving genetic and epigenetic changes in the stem and progenitor cells. The purpose of this study was to identify the stem cell subtypes in GC and examine their clinical relevance. METHODS: Two publicly available datasets were used to identify GC stem cell subtypes, and consensus clustering was performed by unsupervised machine learning methods. The cancer stem cell (CSC) typing-related risk scoring (RS) model was established through multivariate Cox regression analysis. RESULTS: Cross-platform dataset-based two stable GC stem cell subtypes, namely low stem cell enrichment (SCE_L) and high stem cell enrichment (SCE_H), were prudently identified. Gene set enrichment analysis revealed that the classical oncogenic pathways, immune-related pathways, and regulation of stem cell division were active in SCE_H; ferroptosis, NK cell activation, and post-mutation repair pathways were active in SCE_L. GC stem cell subtypes could accurately predict clinical outcomes in patients, tumor microenvironment cell-infiltration characteristics, somatic mutation landscape, and potential responses to immunotherapy, targeted therapy, and chemotherapy. Additionally, a CSC typing-related RS model was established; it was strongly independent and could accurately predict the patient's overall survival. CONCLUSIONS: This study demonstrated the complex oncogenic mechanisms underlying GC. The findings provide a basis and reference for the diagnosis and treatment of GC.