Identification of Hub genes with prognostic values in colorectal cancer by integrated bioinformatics analysis.

BACKGROUND: Our study aimed to investigate the Hub genes and their prognostic value in colorectal cancer (CRC) via bioinformatics analysis. METHODS: The data set of colorectal cancer was downloaded from the GEO database (GSE21510, GSE110224 and GSE74602) for differential expression analysis using the GEO2R tool. Hub genes were screened by protein-protein interaction (PPI) comprehensive analysis. GEPIA was used to verify the expression of Hub genes and evaluate its prognostic value. The protein expression of Hub gene in CRC was analyzed using the Human Protein Atlas database. The cBioPortal was used to analyze the type and frequency of Hub gene mutations, and the effects of mutation on the patients' prognosis. The TIMER database was used to study the correlation between Hub genes and immune infiltration in CRC. Gene set enrichment analysis (GSEA) was used to explore the biological function and signal pathway of the Hub genes and corresponding co-expressed genes. RESULTS: We identified 346 differentially expressed genes (DEGs), including 117 upregulated and 229 downregulated. Four Hub genes (AURKA, CCNB1, EXO1 and CCNA2) were selected by survival analysis and differential expression validation. The protein and mRNA expression levels of AURKA, CCNB1, EXO1 and CCNA2 were higher in CRC tissues than in adjacent tissues. There were varying degrees of immune cell infiltration and gene mutation of Hub genes, especially B cells and CD8+ T cells. The results of GSEA showed that Hub genes and their co-expressed genes mainly participated in chromosome segregation, DNA replication, translational elongation and cell cycle. CONCLUSION: Overexpression of AURKA, CCNB1, CCNA2 and EXO1 had a better prognosis for CRC and this effect was correlation with gene mutation and infiltration of immune cells.

A novel proteomic-based model for predicting colorectal cancer with Schistosoma japonicum co-infection by integrated bioinformatics analysis and machine learning.

Schistosoma japonicum infection is an important public health problem and the S. japonicum infection is associated with a variety of diseases, including colorectal cancer. We collected the paraffin samples of CRC patients with or without S. japonicum infection according to standard procedures. Data-Independent Acquisition was used to identify differentially expressed proteins (DEPs), protein-protein interaction (PPI) network construction, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression) were used to identify candidate genes for diagnosing CRC with S. japonicum infection. To assess the diagnostic value, the nomogram and receiver operating characteristic (ROC) curve were developed. A total of 115 DEPs were screened, the DEPs that were discovered were mostly related with biological process in generation of precursor metabolites and energy,energy derivation by oxidation of organic compounds, carboxylic acid metabolic process, oxoacid metabolic process, cellular respiration aerobic respiration according to the analyses. Enrichment analysis showed that these compounds might regulate oxidoreductase activity, transporter activity, transmembrane transporter activity, ion transmembrane transporter activity and inorganic molecular entity transmembrane transporter activity. Following the development of PPI network and LASSO, 13 genes (hsd17b4, h2ac4, hla-c, pc, epx, rpia, tor1aip1, mindy1, dpysl5, nucks1, cnot2, ndufa13 and dnm3) were filtered, and 3 candidate hub genes were chosen for nomogram building and diagnostic value evaluation after machine learning. The nomogram and all 3 candidate hub genes (hsd17b4, rpia and cnot2) had high diagnostic values (area under the curve is 0.9556). The results of our study indicate that the combination of hsd17b4, rpia, and cnot2 may become a predictive model for the occurrence of CRC in combination with S. japonicum infection. This study also provides new clues for the mechanism research of S. japonicum infection and CRC.

Does the greening of the tax system promote the green transformation of China's heavily polluting enterprises?

Climate change and pollution are the major environmental problems facing the world today. The emission of industrial pollution is not only related to the development of low carbon and green economy but also affects the ecological environment and climate change of human beings. The greening of the tax system is an important reform to help China's green development. From the perspective of internal green innovation and external legal pressure of heavily polluting enterprises, this paper discusses the impact mechanism of implementing the greening of the tax system on the green transformation of heavily polluting enterprises in China and uses DID model to conduct a quasi-natural experiment on the green transformation of heavily polluting enterprises in China. This paper finds that the implementation of the greening of the tax system has a significant impact on the green transformation of China's heavily polluting enterprises; the greening of the tax system policy realizes the "win-win" situation of green environmental governance and the development of heavily polluting enterprises through green technology innovation and forces heavily polluting enterprises in China to conduct environmental protection through the environmental legitimacy pressure. The effect of the greening of the tax system policy has obvious heterogeneity: The greening of the tax system has a more obvious improvement effect on heavily polluting enterprises with low and high market concentration. Compared with state-owned holding enterprises, non-state-owned holding enterprises are more significantly affected by the greening of the tax system. The positive impact of the greening of the tax system on the green transformation of heavily polluting enterprises is mainly reflected in enterprises with low financing costs, while it is not significant in enterprises with high financing costs. This paper enriches the research on the effect of green tax policy, explores solutions based on quasi-nature, and provides policy references for the green transformation of heavily polluting enterprises.