Construction of a TP53 mutation-associated ceRNA network as prognostic biomarkers in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) continues to endanger human health worldwide. Regulatory networks of competing endogenous RNAs (ceRNAs) play important roles in HCC. TP53 is the second most often altered gene in HCC and has a significant role in regulating target genes such as miRNAs and lncRNAs. Methods: Data from patients with TP53 mutation were collected through the cBioPortal database and differential analysis was performed to screen RNAs related to TP53 mutation. The lncRNA-miRNA-mRNA relationship was predicted by the miRcode, miRDB, and TargetScan databases. The ceRNA networks were screened and visualized by Cytoscape. Core ceRNA networks were generated by differential analysis, coexpression analysis, prognostic analysis and subcellular localization. Finally, methylation, mutation, PPI, GSEA, immunity and drug sensitivity analyses of MEX3A were performed to determine the role of MEX3A in HCC. Results: We identified 1508 DEmRNAs, 85 DEmiRNAs and 931 DElncRNAs and obtained a ceRNA network including 28 lncRNAs, 4 miRNAs and 31 mRNAs. Twenty hub DERNAs in the TP53-altered-related ceRNA network were screened out by Cytoscape and the core ceRNA network (LINC00491/TCL6-hsa-miR-139-5p-MEX3A) was obtained by multiple analyses. In addition, we discovered that the methylation level of MEX3A was decreased and the mutation frequency was raised in HCC. Furthermore, elevated MEX3A expression was associated with alterations in the HCC immunological microenvironment. Conclusion: We successfully constructed a reciprocal ceRNA network, which could provide new ideas for exploring HCC mechanisms and therapeutic approaches.

Comprehensive pan-cancer analysis reveals CCDC58 as a carcinogenic factor related to immune infiltration.

CCDC58, a member of the CCDC protein family, has been primarily associated with the malignant progression of hepatocellular carcinoma (HCC) and breast cancer, with limited research conducted on its involvement in other tumor types. We aimed to assess the significance of CCDC58 in pan-cancer. We utilized the TCGA, GTEx, and UALCAN databases to perform the differential expression of CCDC58 at both mRNA and protein levels. Prognostic value was evaluated through univariate Cox regression and Kaplan-Meier methods. Mutation and methylation analyses were conducted using the cBioPortal and SMART databases. We identified genes interacting with and correlated to CCDC58 through STRING and GEPIA2, respectively. Subsequently, we performed GO and KEGG enrichment analyses. To gain insights into the functional status of CCDC58 at the single-cell level, we utilized CancerSEA. We explored the correlation between CCDC58 and immune infiltration as well as immunotherapy using the ESTIMATE package, TIMER2.0, TISIDB, TIDE, TIMSO, and TCIA. We examined the relationship between CCDC58 and tumor heterogeneity, stemness, DNA methyltransferases, and MMR genes. Lastly, we constructed a nomogram based on CCDC58 in HCC and investigated its association with drug sensitivity. CCDC58 expression was significantly upregulated and correlated with poor prognosis across various tumor types. The mutation frequency of CCDC58 was found to be increased in 25 tumors. We observed a negative correlation between CCDC58 expression and the methylation sites in the majority of tumors. CCDC58 showed negative correlations with immune and stromal scores, as well as with NK T cells, Tregs, CAFs, endothelial cells, and immunomodulators. Its value in immunotherapy was comparable to that of tumor mutational burden. CCDC58 exhibited positive correlations with tumor heterogeneity, stemness, DNA methyltransferase genes, and MMR genes. In HCC, CCDC58 was identified as an independent risk factor and demonstrated potential associations with multiple drugs. CCDC58 demonstrates significant clinical value as a prognostic marker and indicator of immune response across various tumor types. Its comprehensive analysis provides insights into its potential implications in pan-cancer research.

Characterization of stem cell subtypes and prognostic signature in hepatocellular carcinoma.

BACKGROUND: Cancer stem cells (CSCs) were linked to cancer aggressiveness and poor prognosis in patients with hepatocellular carcinoma (HCC). METHODS: We integrated two external HCC cohorts to develop the stem cell subtypes according to unsupervised clustering with 26 stem cell gene sets. Between the subtypes, differences in prognosis, clinical characteristics, recognized HCC subtypes, metabolic profile, immune-related features, somatic mutation, and drug sensitivity were examined. The prognostic signature was created, and validated by numerous cohorts, and used to assess the efficacy of immunotherapy and transcatheter arterial chemoembolization (TACE) treatment. The nomogram was developed based on the signature and clinical features. We further examined the function of KIF20A in HCC and proved that KIF20A had the potential to regulate the stemness of HCC cells through western blot. RESULTS: Low stem cell patterns, a good prognosis, positive clinical features, specific molecular subtypes, low metastatic characteristics, and an abundance of metabolic and immunological aspects were associated with Cluster 1, whereas Cluster 2 was the reverse. Chemotherapy and immunotherapy were more effective in Cluster 1. Cluster 1 and CTNNB1 and ALB mutation were more closely. Additionally, the prognosis, immunotherapeutic, and TACE therapy responses were all worse in the high-risk group. The nomogram could predict the survival probability of HCC patients. KIF20A was discovered to be overexpressed in HCC and was revealed to be connected to the stemness of the HepG2 cell line. CONCLUSIONS: Two stem cell subgroups with different prognoses, metabolic, and immunological characteristics in HCC patients were identified. We also created a 7-gene prognostic signature and a nomogram to estimate the survival probability. The function of KIF20A in HCC stemness was initially examined.

Identification of Immune-Associated Genes in Diagnosing Aortic Valve Calcification With Metabolic Syndrome by Integrated Bioinformatics Analysis and Machine Learning.

Background: Immune system dysregulation plays a critical role in aortic valve calcification (AVC) and metabolic syndrome (MS) pathogenesis. The study aimed to identify pivotal diagnostic candidate genes for AVC patients with MS. Methods: We obtained three AVC and one MS dataset from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module gene via Limma and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and random forest) were used to identify candidate immune-associated hub genes for diagnosing AVC with MS. To assess the diagnostic value, the nomogram and receiver operating characteristic (ROC) curve were developed. Finally, immune cell infiltration was created to investigate immune cell dysregulation in AVC. Results: The merged AVC dataset included 587 DEGs, and 1,438 module genes were screened out in MS. MS DEGs were primarily enriched in immune regulation. The intersection of DEGs for AVC and module genes for MS was 50, which were mainly enriched in the immune system as well. Following the development of the PPI network, 26 node genes were filtered, and five candidate hub genes were chosen for nomogram building and diagnostic value evaluation after machine learning. The nomogram and all five candidate hub genes had high diagnostic values (area under the curve from 0.732 to 0.982). Various dysregulated immune cells were observed as well. Conclusion: Five immune-associated candidate hub genes (BEX2, SPRY2, CXCL16, ITGAL, and MORF4L2) were identified, and the nomogram was constructed for AVC with MS diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for AVC in MS patients.

Identification of Molecular Subtypes and a Prognostic Signature Based on Inflammation-Related Genes in Colon Adenocarcinoma.

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package "ConsensusClusterPlus" was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT-PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.

Downregulated Expression of Chromobox Homolog 7 in Hepatocellular Carcinoma.

Background: As an essential member of the Polycomb group (PcG) proteins, chromobox homolog 7 (CBX7) is found deregulated in some human cancers, and is thought to be a contributing factor in carcinogenesis. However, the expression and role of CBX7 in hepatocellular carcinoma (HCC) is still not well characterized. Materials and Methods: The levels of the CBX7 protein were quantified in 75 paired HCC and adjacent nontumor tissues by immunohistochemistry; comparisons were made using McNemar's chi-square test. The Kaplan-Meier estimate was used for survival analysis. Results: We found that the expression of CBX7 in HCC tissues was significantly lower than that of adjacent nontumor tissues. In addition, decreased CBX7 expression levels were correlated with liver cirrhosis in HCC patients. Furthermore, the survival times of HCC patients who were CBX7-expression-negative were shorter than HCC patients who were CBX7-expression-positive. Conclusion: Our results show that downregulation of CBX7 is related to HCC progression and a poor prognosis in HCC patients.

Expression and Clinical Significance of CMTM6 in Hepatocellular Carcinoma.

This study aimed to examine the expression level and clinical significance of chemokine-like factor-like MARVEL transmembrane domain-containing family member 6 (CMTM6) in paired hepatocellular carcinoma (HCC) and adjacent nontumor tissues. The expression of CMTM6 was detected in 75 paired HCC and adjacent nontumor tissues by immunohistochemistry. Chi-square test was used to compare the difference of CMTM6 expression between HCC tissues and adjacent nontumor tissues. The clinic-pathological features and prognosis of HCC patients were collected to analyze the relationship with CMTM6 expression. The positive expression of CMTM6 in HCC tissues was significantly lower than that of adjacent nontumor tissues. The difference of CMTM6 expression between HCC tissues and paired adjacent nontumor tissues was statistically significant (p < 0.05). Furthermore, CMTM6 expression was correlated with HCC metastasis and alpha-fetoprotein (AFP) (p < 0.05). Multivariate logistic regression analysis showed tumor staging, metastasis, and AFP had a significant relationship with CMTM6 expression. In addition, the survival time of HCC patients was different between CMTM6 positive group and CMTM6 negative group by Kaplan-Meier survival analysis (p < 0.05). Downregulation of CMTM6 is related to HCC metastasis and the prognosis of HCC patients.