A Systems Biology Approach to Identify Novel Biomarkers in Progression from Crohn's Disease to Colorectal Cancer

MiRNA polymorphisms affect the prognosis of gastric cancer: insights from Xianyou, Fujian

Introduction

Gastric cancer, characterized by high incidence and substantial disease burden, has drawn continuous attention regarding its occurrence and prognosis. Genetics plays a crucial role in influencing the prognosis of gastric cancer, and single nucleotide polymorphisms are closely associated with the occurrence, development, and prognosis of this malignant tumor. Our study aims to conduct survival analysis on patients carrying different single nucleotide polymorphisms, exploring the relationship between miRNA single nucleotide polymorphisms and the prognosis of gastric cancer.

Methods

Genetic data from 344 patients in Xianyou, Fujian, formed the basis of our study. We delineated the survival rate and median survival time, utilizing the log-rank test and COX regression analysis as statistical tools.

Results

Upon stratifying the data by sex or operation, it was discerned that the GG genotype at MSH2 rs17502941 independently posed a heightened risk for gastric cancer. Other stratification analyses suggested that the subsequent single nucleotide polymorphisms were correlated with patient prognosis: rs17502941, rs884225, rs1468063, rs7143252, and rs2271738.

Discussion

The outcomes of this study strongly suggest that miRNA polymorphisms significantly influence the survival time of gastric cancer patients and can serve as effective predictors for the prognosis of gastric cancer.

Identification of potential crucial genes shared in psoriasis and ulcerative colitis by machine learning and integrated bioinformatics

BACKGROUND

Mounting evidence suggest that there are an association between psoriasis and ulcerative colitis (UC), although the common pathogeneses are not fully understood. Our study aimed to find potential crucial genes in psoriasis and UC through machine learning and integrated bioinformatics.

METHODS

The overlapping differentially expressed genes (DEGs) of the datasets GSE13355 and GSE87466 were identified. Then the functional enrichment analysis was performed. The overlapping genes in LASSO, SVM-RFE and key module in WGCNA were considered as potential crucial genes. The receiver operator characteristic (ROC) curve was used to estimate their diagnostic confidence. The CIBERSORT was conducted to evaluate immune cell infiltration. Finally, the datasets GSE30999 and GSE107499 were retrieved to validate.

RESULTS

112 overlapping DEGs were identified in psoriasis and UC and the functional enrichment analysis revealed they were closely related to the inflammatory and immune response. Eight genes, including S100A9, PI3, KYNU, WNT5A, SERPINB3, CHI3L2, ARNTL2, and SLAMF7, were ultimately identified as potential crucial genes. ROC curves showed they all had high confidence in the test and validation datasets. CIBERSORT analysis indicated there was a correlation between infiltrating immune cells and potential crucial genes.

CONCLUSION

In our study, we focused on the comprehensive understanding of pathogeneses in psoriasis and UC. The identification of eight potential crucial genes may contribute to not only understanding the common mechanism, but also identifying occult UC in psoriasis patients, even serving as therapeutic targets in the future.

The role of intestinal microecology in inflammatory bowel disease and colorectal cancer: A review

Intestinal microecology is a dominant and complex microecological system in human body. Generally, intestinal microecosystem consists of normal symbiotic flora and its living environment (including intestinal epithelial tissue and intestinal mucosal immune system). Commensal flora is the core component of microecology. Both structures of intestinal mucosa and functions of immune system are essential to maintain homeostasis of intestinal microecosystem. Under normal conditions, intestinal microorganisms and intestinal mucosa coordinate with each other to promote host immunity. When certain factors in the intestine are altered, such as disruption of the intestinal barrier causing dysbiosis of the intestinal flora, the immune system of the host intestinal mucosa makes a series of responses, which leads to the development of intestinal inflammation and promotes colorectal cancer. In this review, to further understand the relationship between intestinal microecology and intestinal diseases, we systematically elaborate the composition of the intestinal mucosal immune system, analyze the relationship between intestinal flora and mucosal immune system, and the role of intestinal flora on intestinal inflammatory diseases and colorectal cancer.

A pan-cancer analysis indicates long noncoding RNA HAND2-AS1 as a potential prognostic, immunomodulatory and therapeutic biomarker in various cancers including colorectal adenocarcinoma

The HAND2-AS1 (HAND2 Antisense RNA 1) Long noncoding RNA (lncRNA) has emerged as a participant in the initiation of various cancer types, underscoring its pivotal involvement in both oncological processes and immune responses. To gain deeper insights into the functional nuances of HAND2-AS1 and identify novel avenues for cancer immunotherapy, a comprehensive evaluation of this gene was undertaken. Here, based on the co-expression network analysis and construction of interacting lncRNA-mRNA genes, we introduce the HAND2-AS1 lncRNA, emphasizing its key roles in tumorigenesis and immune regulation. Our study spans across 33 distinct cancer types, revealing the HAND2-AS1's aberrant expression patterns, methylation variations, mutational signatures, and immune engagement. Across a majority of tumors, HAND2-AS1 exhibited a propensity for down-regulation, remarkably an association with poor survival outcomes. The outcomes of functional enrichment analyses strongly suggest HAND2-AS1's engagement in tumor progression and its association with various immune pathways across diverse tumor classifications. Additionally, a positive correlation emerged between HAND2-AS1 expression and the infiltration levels of key immune cells, encompassing not only immunosuppressive entities such as tumor-associated macrophages, cancer-associated fibroblasts, and Tregs, but also immune effector cells like NK cells and CD8+ T cells, spanning a pan-cancer context. Furthermore, the differential expression of HAND2-AS1 appears to have downstream consequences on various pathways, thus implicating it as a potential regulator in diverse cancer types. Finally, we have employed CRC tumor and normal samples to carry out clinical validation of HAND2-AS1. Our study unveils HAND2-AS1's potential as a pan-cancer tumor suppressor, and its essential role in the tumorigenesis and immune surveillance. The increased HAND2-AS1 expression emerges as a promising candidate for prognostic evaluation, therapeutic strategy, and a focal point for immunotherapeutic interventions.

Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation

Colorectal cancer (CRC) liver metastasis accounts for the majority of fatalities associated with CRC. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate CRC metastasis-related biomarkers by employing a machine learning (ML) approach and experimental validation. The gene expression profile of CRC patients with liver metastasis was obtained using the GSE41568 dataset, and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify the most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental validation was done through qRT-PCR. 11 genes were commonly selected by LASSO and P-SVM algorithms, among which seven had prognostic value in colorectal cancer. It was found that the expression of the MMP3 gene decreases in stage IV of colorectal cancer compared to other stages (P value < 0.01). Also, the expression level of the WNT11 gene was observed to increase significantly in this stage (P value < 0.001). It was also found that the expression of WNT5a, TNFSF11, and MMP3 is significantly lower, and the expression level of WNT11 is significantly higher in liver metastasis samples compared to primary tumors. In summary, this study has identified a set of potential biomarkers for CRC metastasis using ML algorithms. The findings of this research may provide new insights into identifying biomarkers for CRC metastasis and may potentially lay the groundwork for innovative therapeutic strategies for treatment of this disease.

Identification of immune-related hub genes contributing to the pathogenesis, diagnosis, and remission of ulcerative colitis by integrated bioinformatic analyses

The inflammatory disease ulcerative colitis (UC) is multifaceted, immune-mediated, chronic, and relapsing, which is considered to be mainly driven by dysregulated mucosal immune response. The remission of the inflammatory response is a marker of mucosal healing, relating to the low risk of hospitalizations, colorectal cancer, and colectomy. In spite of this, it is still unclear what the key immunological mechanism is which contributes to UC. Here, we explored the immune mechanism and related key genes underlying the state of inflammation in UC. Co-expression networks were constructed based on the expression profiles of immune-related genes in GSE179285. Using Weighted Gene Co-expression Network Analysis and Protein-protein interactions analysis, common hub genes were identified in the module of interest. Then, screening of real hub genes, significantly differentially expressing in inflamed UC, was carried out by Differential Expression Genes Analysis of GSE75214, GSE53306, and GSE6731datasets and immunohistochemistry of clinical samples. The diagnosis Capacity of the hub gene was identified by "glm" function in R. The potential key immune-related mechanisms were investigated using functional enrichment analysis and gene set enrichment analysis (GSEA). Bioinformatics tools were used to predict potential upstream transcription factors (TF), including the UCSC genome browser, correlation analyses, and JASPAR browser. The analysis revealed the blue module, consisting of 227 immune-related genes, showed the highest correlation with inflamed UC. And then, forty-three common candidates were distinguished. S100A9 was identified within the key module as a real hub gene with good diagnostic performance. The immune genes in the blue module were markedly enriched in the Cytokine-Cytokine receptor interaction. S100A9 most likely gets involved NOD-like receptor (NLR) signaling pathway. SPI1 showed the strongest likelihood to be the regulator. S100A9 was identified as the real immune-related hub gene for inflamed UC. Both diagnosis and remission may be aided by its high expression in the inflamed UC.