3 plus pure health letter: uncovering the single gene mining trap.

Click [medical side] to pay attention to us. In recent years, the data mining of student credit has been more and more recognized. Many people have successfully published SCI articles by using the resources of public database, which paves the way for their graduation and promotion.although the number of articles on Shengxin has increased sharply and it is more and more difficult to send out pure Shengxin articles, many such articles appear every year.compared with the time-consuming and laborious wet experiments, the articles of pure Shengxin seem to be more popular among the groups in urgent need of graduation and promotion. Today, I would like to share with you an article on the single gene mining of 3 + pure Shengxin published in June this year. The article has a clear idea and is not difficult to operate.the purpose of this article is to clarify the possible relationship between the expression of prkacb and the outcome of CRC patients. The authors identified and verified the expression of prkacb in normal tissues and tumor tissues of patients with colorectal cancer by using GEO, oncomine and TCGA databases. The clinicopathological data and survival analysis were conducted based on TCGA spectrum, hoping to provide useful insights into the occurrence and development of colorectal cancer.data sources: Download seven data sets containing CRC tumor and non tumor tissues from geo database: gse110225, gse32323, gse44076, gse9348, gse41328, gse2510, gse68468; download colorectal cancer related mRNA data from TCGA database; download clinical data of CRC patients and prkacb RNA seq V2 data from cbioportal database.verify in oncomine database.results the expression of prkacb was different. Firstly, the expression of prkacb in normal tissues and tumor tissues of patients with colorectal cancer was analyzed by geo database and TCGA database, and it was found that prkacb was low expression in CRC tumor tissues.next, in order to verify the above results, the authors set p value ≤ 1e-4, multiple change ≥ 2, and gene ranking in the top 10% as the threshold value in oncomine database. The meta analysis of prkacb expression in 15 studies also confirmed that prkacb was low expression in CRC tumor tissues.2 the relationship between prkacb and patient survival was found to be different in CRC tumor tissues and normal tissues. Next, the authors downloaded temporary TCGA data from cbioportal database to analyze the impact of low prkacb expression on the survival rate of patients. It was found that the low expression of prkacb in tumor tissues was related to the poor disease-free survival rate and overall survival rate; meanwhile, the low expression of prkacb was found One year, three years and five years of OS were all high risk factors.in addition, subgroup survival analysis was carried out in different populations.it was found that low expression of prkacb was a risk factor for male, white, non mucinous adenocarcinoma patients and stage III-IV patients.3prkacb was associated with clinicopathological features of CRC patients. Next, the authors also used TCGA data downloaded from cbioportal database to analyze the association between rkacb and clinicopathological features of CRC patients. It was found that only age and gender were related to prkacb expression level, while BMI, tumor stage, lymph node stage, metastasis stage, AJCC stage, lymph node invasion, and nerve function were found There was no significant difference in prkacb expression between invasion, vascular invasion, race and tumor status (P & gt; 0.05).4kegg / go biological process enrichment. Next, the author identified prkacb related genes using mRNA data downloaded from TCGA official website. According to the Spearman coefficient (- 1 to 1) between differentially expressed genes and prkacb, the genes with P & lt; 0.05 were selected as prkacb related genes. The enrichment analysis of these prkacb related genes was carried out.PS: under normal circumstances, single gene can not be used for functional enrichment analysis, but the author screened out the related gene groups of prkacb from the differentially expressed genes, and the function of prkacb can be indirectly analyzed through enrichment analysis of these related gene groups.the author's "from this to that" analysis method is worth learning.5 protein to protein interaction (PPI) network construction in this step, the author also used prkacb related genes.the prkacb related genes were uploaded to the string website to analyze the interaction between proteins. The interaction score of 0.400 (moderately credible) was regarded as the cut-off standard. delete protein nodes that do not interact with other proteins. then, PPI pairs were input into the Cytoscape software to construct a PPI network, and the top 10 Central genes (prkacb, atp2b2, MAPT, phlpp2, abccb, grin2a, MYLK, gria1, BChE, adcy2) were determined according to the level of cytohabba. 6 gene co expression network analysis. Finally, the author used the co expression online analysis function of cbioportal database for gene co expression analysis, and drew the gene co expression network by using the software of Cytoscape. analysis showed that prkacb was positively correlated with fam167a, nrip3, rasl11b, st13p4 and tmem99, and negatively correlated with ALPP, c3orf70, JunD and zbtb7a. as the author described in the discussion section, studies have confirmed that nrip3, rasl11b play an anti-cancer role in breast cancer and renal cell carcinoma, and ALPP, JunD, zbtb7a promote cancer progression in gastric cancer, prostate cancer, breast cancer and other cancers. therefore, the authors speculate that prkacb and these tumor suppressor factors play a synergistic role in inhibiting tumor growth, while prkacb and those genes that promote tumor genesis antagonize the anti-tumor effect of prkacb. that is to say, prkacb may play a role as a tumor suppressor in tumor tissue. after summarizing the ideas, this article has been analyzed. Let's sort out the analysis ideas: in fact, compared with the general Shengxin irrigation routine, the analysis content of this article is relatively rich. Of course, if you want to sprint to higher scores, you can carry out clinical data validation, take tissue samples of patients to verify the expression level of target gene, and analyze The relationship between them and pathological data of patients. it is not difficult to find that the 1-2 score Shengxin articles only need simple TCGA or geo database analysis, while the 3-5 Shengxin articles need the joint use of multiple databases. If you want to sprint 5 + articles, you need to carry out subsequent cell level or even animal level experiments to verify. if you happen to have a certain target gene in your hand, you can not only get a 3 + SCI article without doing an experiment, but also provide data support for subsequent experiments. Why not? The solicitation notice "medical prescription" is now officially open to fans! 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