MMP14 is a diagnostic gene of intrahepatic cholangiocarcinoma associated with immune cell infiltration.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor of the hepatobiliary system with concealed onset, strong invasiveness and poor prognosis. AIM: To explore the disease characteristic genes that may be helpful in the diagnosis of ICC and affect immune cell infiltration. METHODS: We downloaded two ICC-related human gene expression profiles from GEO database as the training group (GSE26566 and GSE32958 datasets) for difference analysis, and performed enrichment analysis on differential genes. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), three machine learning algorithms, were used to screen the characteristic genes. Double verification was carried out on GSE107943 and The Cancer Genome Atlas, two verification groups. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the diagnostic efficacy of genes for ICC. CIBERSORT and ssGSEA algorithms were used to evaluate the effect of characteristic genes on immune infiltration pattern. Human Protein Atlas (HPA) was used to analyze the protein expression level of the target gene. RESULTS: A total of 1091 differential genes were obtained in the training group. Enrichment analysis showed that the above genes were mainly enriched in small molecular catabolism, complement and coagulation cascade, bile secretion and other functions and pathways. Twenty-five characteristic genes were screened by LASSO regression, 19 by SVM-RFE algorithm, and 30 by RF algorithm. Three algorithms were used in combination to determine the characteristic gene of ICC: MMP14. The verification group confirmed that the genes had a high diagnostic accuracy (AUC values of the training group and the verification group were 0.960, 0.999, and 0.977, respectively). Comprehensive analysis of immune infiltration showed that MMP14 could affect the infiltration of monocytes, activated memory CD4 T cells, resting memory CD4 T cells, and other immune cells, and was closely related to the expression of CD200, cytotoxic T-lymphocyte-associated antigen 4, CD14, CD44, and other immune checkpoints. The results of immunohistochemistry in HPA database showed was indeed overexpressed in ICC. CONCLUSION: MMP14 can be used as a disease characteristic gene of ICC, and may regulate the distribution of immune-infiltrating cells in the ICC tumor microenvironment, which provides a new method for the determination of ICC diagnostic markers and screening of therapeutic targets.

Construction of a competing endogenous RNA network related to the prognosis of cholangiocarcinoma and comprehensive analysis of the immunological correlation.

BACKGROUND: Cholangiocarcinoma (CCA) is a malignant tumor of the digestive system, with occult onset in the early stage, a high degree of malignancy in the late stage, and poor prognosis. At present, the pathogenesis of CCA is not clear, and there is a lack of effective immunotherapy. The purpose of this study was to identify the potential regulatory mechanism of CCA and analyze the possibility of its related immunotherapy. METHODS: The circular RNAs (circRNAs) expression profile data of CCA was downloaded from the Gene Expression Omnibus (GEO) database; the miRNA and mRNA expression profile data of CCA were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic factors were screened by univariate Cox regression analysis, and the competing endogenous RNA (ceRNA) network was constructed via survival analysis. Multivariate Cox analysis was used to screen the independent prognostic factors and construct a prognostic correlation subnetwork. Analyzing the tumor microenvironment of CCA and survival analysis were performed according to the score of the microenvironment, and the distribution of tumor infiltrating immune cells (TICs) in CCA was calculated using the CIBERSORT algorithm. We explored the expression pattern of the target genes in pan-cancer, and the correlation between the key genes in the ceRNA subnetwork, TICs and immune checkpoints was analyzed using an online database. Finally, the expression levels of target genes were validated based on the Human Protein Atlas (HPA) databases. RESULTS: We screened four circRNAs, 10 miRNAs, and 17 mRNAs with significant differences, and constructed the ceRNA network. Independent prognostic factors were screened by multivariate Cox regression analysis, and a subnetwork containing five nodes (hsa_circ_0002073→hsa-mir-4524a-3p→SLC16A3/SLC35E4/DDX4) was constructed. Further analysis showed that SLC16A3 was not only an independent posterior factor of CCA, but was also closely correlated with immune cells, immune checkpoints, and immunotherapy, and had a certain regulatory effect on the tumor microenvironment. CONCLUSIONS: Our study identified a novel prognostic marker of CCA, SLC16A3, and revealed the regulatory role of SLC16A3 in the tumor microenvironment, which is expected to provide new insights for the early diagnosis, prognosis, and targeted therapy of CCA.

Comprehensive Analysis of Inflammatory Response-Related Genes, and Prognosis and Immune Infiltration in Patients With Low-Grade Glioma.

Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG. Methods: We downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognosis-related differentially expressed inflammatory genes with the difference analysis and single-factor Cox regression analysis. The prognostic risk model was constructed by LASSO Cox regression analysis, which enables us to compare the overall survival rate of high- and low-risk groups in the model by Kaplan-Meier analysis and subsequently draw the risk curve and survival status diagram. We analyzed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. The CellMiner database was used to study drug sensitivity. Results: In this study, 3 genes (CALCRL, MMP14, and SELL) were selected from 9 prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model was better. The age, grade, and risk score can be used as independent prognostic factors (p < 0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. We found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method (p < 0.05). Finally, we explored the relationship between the genes in the model and the susceptibility of drugs. Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.

Differential analysis of RNA methylation regulators in gastric cancer based on TCGA data set and construction of a prognostic model.

BACKGROUND: Methylation is one of the common forms of RNA modification, which mainly include N6-methyladenosine (m6A), C5-methylcytidine (m5C), and N1-methyladenosine (m1A). Numerous studies have shown that RNA methylation is associated with tumor development. We aim to construct prognostic models of gastric cancer based on RNA methylation regulators. METHODS: The transcriptome and clinical data of gastric cancer and normal samples were obtained from the National Cancer Institute Genome Data Commons (NCI-GDC). Use Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to construct risk models for different types of RNA methylation. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive efficiency of risk characteristics. Cluster heat maps are used to assess the correlation with clinical information. Univariate and multivariate Cox analyses were used to analyze prognostic effects of risk scores. Gene Set Enrichment Analysis (GSEA) analyzes the functional enrichment of RNA methylation genes. And make a separate analysis of the data of Asians. RESULTS: The expression of most of the 30 RNA methylation regulators were significantly different in cancer and paracancerous tissues (P<0.05). Three methylated genes (FTO, ALKBH5, and RBM15) were screened from m6A by LASSO Cox regression analysis. Five methylated genes (FTO, ALKBH5, TRMT61B, RBM15, and YXB1) were selected from the population, and were used to construct two risk ratio models. Survival analysis showed that the survival rate of patients in the low-risk group was significantly higher than that in the high-risk group (P<0.05). All ROC curves indicated that the predictive efficiency of risk characteristics was good [area under the ROC curve (AUC): 0.6-1].Cluster analysis reveals differences in clinical data between the two groups. Univariate and multivariate Cox regression results show that the risk score has independent prognostic value. GSEA showed that pathways such as cell cycle were significantly enriched in the low-risk group, while pathways such as calcium signaling pathway were significantly enriched in the high-risk group. In addition, three methylation models that can predict the prognosis of Asian gastric cancer patients were obtained. CONCLUSIONS: The methylation prognosis model constructed in this study can effectively predict the prognosis of gastric cancer patients.

FAT10 promotes hepatocellular carcinoma (HCC) carcinogenesis by mediating P53 degradation and acts as a prognostic indicator of HCC.

BACKGROUND: With the advancement of hepatocellular carcinoma (HCC) treatment technology, the treatment options for HCC patients have increased. However, due to high heterogeneity, among other reasons, the five-year survival rate of patients is still very low. Currently, gene expression prognostic models can suggest more appropriate strategies for the treatment of HCC. This study investigates the role of FAT10 in hepatocarcinogenesis and its underlying mechanism. METHODS: The expression of FAT10 was detected by immunohistochemical method using tissue arrays containing 4 specimens of patients with digestive cancer. The expression of FAT10 was determined by a tissue microarray which included 286 pairs of HCC samples and corresponding normal mucosae and was further confirmed by real-time polymerase chain reaction (PCR) and western blot. The Kaplan-Meier survival curve was used to determine the correlation of FAT10 expression with patients' recurrence and overall survival (OS) rate. In vivo, liver fibrosis, cirrhosis, and HCC models were established to assess the FAT10 expression. Moreover, FAT10 over-expressing cell lines were used to determine the molecular mechanism underlying the FAT10-induced cell proliferation and hepatocarcinogenesis by reporter gene measure, real-time PCR, and western blot. Based on TCGA database, signal pathways associated with FAT10 and HCC invasion and metastasis were analyzed by KEGG enrichment analyze. RESULTS: Overexpression of FAT10 in HCC was observed in this study compared with its expression in other digestive tumors. Clinicopathological analysis revealed that FAT10 expression levels were closely associated with tumor diameters and poor prognosis of HCC. This study also confirmed through in vivo experiments that the expression of FAT10 in liver fibrosis, cirrhosis, and HCC gradually increases. Further study revealed that forced FAT10 expression enhanced the growth ability of HCC cells and mediated the degradation of the critical anti-cancer protein p53, which led to carcinogenesis. Finally, 9 signal pathways related to HCC metastasis were obtained through bioinformatics analysis. CONCLUSIONS: FAT10 may act as a proto-oncogene that facilitates HCC carcinogenesis by mediating p53 degradation, and the expression of FAT10 is negatively correlated with the prognosis of HCC patients. FAT10 is expected to become a potential combined target and prognostic warning marker for HCC treatment.

Construction of a ceRNA network in hepatocellular carcinoma and comprehensive analysis of immune infiltration patterns.

BACKGROUND: Hepatocellular carcinoma (HCC) is a type of refractory malignant tumor with high fatality rate. Currently, immunotherapy and competitive endogenous RNA (ceRNA) are research hotspots in HCC, but the relationship between ceRNA and the immune microenvironment in HCC is unclear. METHODS: Firstly, a differentially expressed circRNA-miRNA-mRNA network was constructed from the GEO database, and functional enrichment analysis was performed. Next, combine the TCGA database to construct a ceRNA prognosis-related subnetwork. Establish a risk prediction model based on the mRNA in the sub-network, and evaluate the impact of the model on the prognosis. Use clinical samples to verify the expression of genes in the model. Finally, we analyzed the distribution of tumor infiltrating immune cells (TIC) in HCC, and explored the correlation between mRNAs in the ceRNA sub-network and immune infiltration. RESULTS: We used the HCC ceRNA network (including 12 circRNA, 5 miRNA, and 8 mRNA) as a starting point for the identification of target genes (PSMD10, ESR1 and PPARGC1A) in the ceRNA prognosis-related subnetwork to establish a risk prediction model and elucidated its important role in predicting the poor prognosis of HCC. The differences in mRNA expression verified by clinical samples are consistent with the database. In addition, we found that the mRNAs in the ceRNA prognosis subnetwork are closely related to different types of TICs and immune checkpoints. CONCLUSIONS: This study is expected to serve as a reference for the study of mechanisms underlying liver cancer, the screening of prognostic markers and the evaluation of the immune response.

Prognostic factors for laryngeal sarcoma and nomogram development for prediction: a retrospective study based on SEER database.

BACKGROUND: Laryngeal sarcoma is an extremely rare malignant tumor of larynx and usually reported as case reports or small series. At present, there is no research based on big data about the prognostic factors affecting laryngeal sarcoma. Our study aimed to investigate the prognostic survival factors of laryngeal sarcoma and develop a comprehensive nomogram for predicting the survival of laryngeal sarcoma. METHODS: Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database to find patients diagnosed with laryngeal sarcoma from 1998 to 2016. The data were obtained using SEER Stat 8.3.5 software, collated, and analyzed by Excel 2016 software and SPSS (v25.0). Kaplan-Meier curves were used for survival analysis. The variables obtained by univariate analysis were introduced into the Cox proportional hazard model for multivariate analysis. The risk factors affecting the prognosis of laryngeal sarcoma were obtained (P<0.05 indicated statistical significance). The independent prognostic factors of laryngeal sarcoma were integrated and used to construct a nomogram. RESULTS: A total of 381 patients with laryngeal sarcoma were included. The median age of diagnosis was 67 years. The proportion of patients who had received surgical treatment was 62.73%, while 22.31% of patients had received no surgery. The 1-, 3-, 5-, and 10-year survival rates were 87%, 76%, 61%, and 45%, respectively. The median survival time was 102.35 months. Univariate analysis showed that increased age, primary site, pathology, pathological grade, and surgical treatment were significantly correlated with patient survival time and were risk factors for the patients' prognosis. Race, gender, and even lymph node metastasis were not significantly correlated with patient prognosis. The risk factors obtained from the univariate analysis were incorporated into the Cox risk model for multivariate analysis, the independent risk factors for prognosis of patients were: age (HR 1.569, 95% CI: 1.358-1.813, P<0.005), pathology (HR 0.834, 95% CI: 0.734-0.948, P<0.005), pathological grade (HR 1.433, 95% CI: 1.164-1.764, P<0.001), surgical treatment (HR 0.778, 95% CI: 0.696-0.870, P<0.000), primary site was excluded (P=0.092). We included all the risk factors from the multi-factor analysis to construct a nomogram, and the C-index value was 0.73, indicating that it was well-calibrated in the medium and long term. CONCLUSIONS: Laryngeal sarcoma is a rare malignant tumor of the larynx, which most often affects people between the ages of 50 and 79 and males. Our study shows that age, pathology, pathological grade, surgical treatment may be the risk factors for patients' prognosis. Based on this, we constructed a nomogram model with a prediction accuracy of 73% that could help clinicians make decisions on an individual basis.