Background: Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patient’s quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear.Methods: We constructed a ceRNA network associated with KIRC by analyzing the long non-coding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from The Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts.” Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism.Results: We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [area under curves (AUCs) of 1, 3, and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747, and 0.772; AUCs of 1, 3, and 5-year survivals in nomogram based on immune cells: 0.603, 0.642, and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells.Conclusion: Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.
BackgroundThe association between environmental and socioeconomic risk factors and the occurrence of hepatocellular carcinoma (HCC) are still inconclusive. A meta-analysis was conducted to address this issue.MethodsWe systematically searched the databases including PubMed, Web of Science, and Google Scholar and collected the related risk factors of HCC before March 6, 2020. Statistical analysis was performed on the odds ratio (OR) value and 95% CI of the correlation between environmental and socioeconomic factors and HCC. Begg's rank correlation test, Egger's linear regression test, and the funnel plot were employed for identification of the publication bias.ResultsOut of 42 studies, a total of 57,892 participants were included. Environmental and socioeconomic risk factors including ever educated (illiteracy); race (Black, Hispanic, and Asian); medium and low incomes; occupations (farmer and labor); passive smoking; place of residence (rural); blood aflatoxin B1 (AFB1) adduct level; exposure of pesticide, etc., were statistically increased with the occurrence of HCC (P < 0.05) and OR values and 95% CIs were 1.37 (1.00, 1.89), 2.42 (1.10–5.31), 1.90 (0.87–4.17), 5.36 (0.72–40.14), 1.48 (1.11, 1.96), 1.74 (1.00–3.03), 1.49 (1.06–2.08), 1.52 (1.07–2.18), 1.43 (0.27, 7.51), 1.46 (1.09, 1.96), 2.58 (1.67–3.97), and 1.52 (0.95–2.42), respectively. We found 6–9, 9–12, and ≥12 years of education that statistically reduced the risk of the occurrence of HCC (P < 0.05) and OR values and 95% CIs were 0.70 (0.58, 0.86), 0.52 (0.40, 0.68), and 0.37 (0.23, 0.59), respectively. No significant associations (P > 0.05) were observed between race (Hispanic and Asian), passive smoking, marital status, place of birth, place of residence, and HCC. In stratified analysis, exposure of pesticide was statistically significant (P < 0.05), while race of black was on the contrary.ConclusionEnvironmental and socioeconomic risk factors have great impacts on the incidence rate of HCC. Improving national education and income levels can significantly reduce the risk of HCC.PROSPERO Registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42020151710.
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