Integrative meta-analysis for the identification of hub genes in chemoresistant colorectal cancer

Prediction and verification of the prognostic biomarker SLC2A2 and its association with immune infiltration in gastric cancer

Gastric cancer (GC) is the fifth most common cause of cancer-associated deaths; however, its treatment options are limited. Despite clinical improvements, chemotherapy resistance and metastasis are major challenges in improving the prognosis and quality of life of patients with GC. Therefore, effective prognostic biomarkers and targets associated with immunological interventions need to be identified. Solute carrier family 2 member 2 (SLC2A2) may serve a role in tumor development and invasion. The present study aimed to evaluate SLC2A2 as a prospective prognostic marker and chemotherapeutic target for GC. SLC2A2 expression in several types of cancer and GC was analyzed using online databases, and the effects of SLC2A2 expression on survival prognosis in GC were investigated. Clinicopathological parameters were examined to explore the association between SLC2A2 expression and overall survival (OS). Associations between SLC2A2 expression and immune infiltration, immune checkpoints and IC50 were estimated using quantification of the tumor immune contexture from human RNA-seq data, the Tumor Immune Estimation Resource 2.0 database and the Genomics of Drug Sensitivity in Cancer database. Differential SLC2A2 expression and the predictive value were validated using the Human Protein Atlas, Gene Expression Omnibus, immunohistochemistry and reverse transcription-quantitative PCR. SLC2A2 expression was downregulated in most types of tumor but upregulated in GC. Functional enrichment analysis revealed an association between SLC2A2 expression and lipid metabolism and the tumor immune microenvironment. According to Gene Ontology term functional enrichment analysis, SLC2A2-related differentially expressed genes were enriched predominantly in 'chylomicron assembly', 'plasma lipoprotein particle assembly', 'high-density lipoprotein particle', 'chylomicron', 'triglyceride-rich plasma lipoprotein particle', 'very-low-density lipoprotein particle'. 'intermembrane lipid transfer activity', 'lipoprotein particle receptor binding', 'cholesterol transporter activity' and 'intermembrane cholesterol transfer activity'. In addition, 'cholesterol metabolism', and 'fat digestion and absorption' were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Patients with GC with high SLC2A2 expression had higher levels of neutrophil and M2 macrophage infiltration and a significant inverse correlation was observed between SLC2A2 expression and MYC targets, tumor mutation burden, microsatellite instability and immune checkpoints. Furthermore, patients with high SLC2A2 expression had worse prognosis, including OS, disease-specific survival and progression-free interval. Multivariate regression analysis demonstrated that SLC2A2 could independently prognosticate GC and the nomogram model showed favorable performance for survival prediction. SLC2A2 may be a prospective prognostic marker for GC. The prediction model may improve the prognosis of patients with GC in clinical practice, and SLC2A2 may serve as a novel therapeutic target to provide immunotherapy plans for GC.

Hepatocyte nuclear factor 4 a (HNF4α): A perspective in cancer

HNF4α, a transcription factor, plays a vital role in regulating functional genes and biological processes. Its alternative splicing leads to various transcript variants encoding different isoforms. The spotlight has shifted towards the extensive discussion on tumors interplayed withHNF4α abnormalities. Aberrant HNF4α expression has emerged as sentinel markers of epigenetic shifts, casting reverberations upon downstream target genes and intricate signaling pathways, most notably with cancer. This review provides a comprehensive overview of HNF4α's involvement in tumor progression and metastasis, elucidating its role and underlying mechanisms.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.