POC1A acts as a promising prognostic biomarker associated with high tumor immune cell infiltration in gastric cancer

Analysis of the role of POC1A in the development and progression of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers worldwide. POC1 centriolar protein A (POC1A) is a gene encoding a protein that plays a key role in the centrosome, and is one of the two isoforms of POC1. To date, the expression of POC1A in HCC and its potential as a biomarker and tumor therapeutic target have not been examined. This study aimed to explore the effect of POC1A on patients with HCC and its potential mechanism.

Methods

This study investigated the role of POC1A in the occurrence and development of HCC. It analyzed the expression of POC1A in various types of HCC patients and its effect on survival using HCC patient information from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), the Human Protein Atlas (HPA), and the Hepatocellular Carcinoma Cell DataBase (HCCDB). It then explored the major enrichment pathways and gene functions of POC1A in HCC using the gene set enrichment analysis (GSEA) method and examined its protein-protein interactions (PPIs). Finally, it predicted the potential transcription factors (TFs) and target microRNAs (miRNAs) of POC1A, and analyzed the single nucleotide variation (SNV) and copy number variation (CNV) mutations of POC1A and the related genes in HCC, as well as their effects on immune cells.

Results

The results showed that POC1A was significantly overexpressed in HCC and was significantly associated with a poor prognosis. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that POC1A was mainly involved in the regulation of cell-cycle pathways and chromosome segregation functions. POC1A showed significant interactions with NUDC and PPARG, and they both had different numbers of SNV and CNV mutations in the HCC samples. In relation to immunity, the high expression of POC1A and its reciprocal genes may play an important role in B cells and macrophages.

Conclusions

In general, our findings suggest that POC1A overexpression could have an important effect on the development of HCC by regulating cell-cycle pathways, and that it could serve as a novel prognostic biomarker and a potential therapeutic target for HCC.

Clinical and molecular analysis of cilia-associated gene signature for prognostic prediction in glioma

PURPOSE

Glioma is a highly malignant and unfavorable cancer in the brain. Recent evidence highlights the vital role of cilia-related pathways as novel regulators of glioma development. However, the prognostic potential of ciliary pathways in glioma is still ambiguous. In this study, we aim to construct a gene signature using cilia-related genes to facilitate the prognostication of glioma.

METHODS

A multi-stage approach was employed to build the ciliary gene signature for prognostication of glioma. The strategy involved the implementation of univariate, LASSO, and stepwise multivariate Cox regression analyses based on TCGA cohort, followed by independent validation in CGGA and REMBRANDT cohort. The study further revealed molecular differences at the genomic, transcriptomic, and proteomic levels between distinct groups.

RESULTS

A prognostic tool utilizing a 9-gene signature based on ciliary pathways was developed to assess the clinical outcomes of glioma patients. The risk scores generated by the signature demonstrated a negative correlation with patient survival rates. The validation of the signature in an independent cohort reinforced its prognostic capabilities. In-depth analysis uncovered distinctive molecular characteristics at the genomic, transcriptomic, and protein-interactive levels in the high- and low-risk groups. Furthermore, the gene signature was able to predict the sensitivity of glioma patients to conventional chemotherapeutic drugs.

CONCLUSION

This study has established the utility of a ciliary gene signature as a reliable prognostic predictor of glioma patient survival. Findings not only enhance our comprehension of the intricate molecular mechanisms of cilia pathways in glioma, but also hold significant clinical implications in directing chemotherapeutic strategies.

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer.

POC1A, prognostic biomarker of immunosuppressive microenvironment in cancer

POC1 centriolar protein A (POC1A) effect in pan-cancer remains uncertain. The POC1A expression in normal and tumor tissues underwent analysis in this study utilizing data from the Genotype-Tissue Expression (GTEx) project and the Cancer Genome Atlas (TCGA) database. POC1A prognostic value and clinicopathological features were assessed utilizing the TCGA cohort. The relationship between immunological cell infiltration and POC1A of TCGA samples downloaded from TIMER2 and ImmuCellAI databases were observed. The relation between POC1A and immunological checkpoints genes, microsatellite instability (MSI) as well as tumor mutation burden (TMB) was also evaluated. Additionally, gene set enrichment analysis (GSEA) was utilized for exploring POC1A potential molecular mechanism in pan-cancer. In almost all 33 tumors, POCA1 showed a high expression. In most cases, high POC1A expression was linked significantly with a poor prognosis. Additionally, Tumor immune infiltration and tumors microenvironment were correlated with the expression of POC1A. In addition, TMB and MSI, as well as immune checkpoint genes in pan-cancer, were related to POC1A expression. In GSEA analysis, POC1A is implicated in cell cycle and immune-related pathways. These results might elucidate the crucial roles of POC1A in pan-cancer as a prognostic biomarker and immunotherapy target.

ADAM12 as a Clinical Prognostic Indicator Associated with Tumor Immune Infiltration in Lung Adenocarcinoma

Twenty-two functional α-disintegrin and metalloproteinases (ADAMs) have been identified in humans, 12 of which have proteolytic activity. The role of ADAMs in cancer has attracted increasing attention. However, the expression and significance of ADAMs in lung adenocarcinoma (LUAD) remain unclear. Most recently, we investigated the transcriptional data of ADAMs and related overall survival in patients with LUAD based on several databases, including TCGA, cBioPortal, Kaplan-Meier Plotter, LinkedOmics, KEGG, TIMER, and TISIDB. Knockdown of ADAM12 was performed in vitro to verify its biological function. According to our findings, 10 ADAMs exhibited significant differential expression in LUAD compared with cancer-adjacent normal tissues. ADAM12 expression was significantly higher in LUAD tissues than in paracancerous tissues, and lower ADAM12 expression was associated with better survival. Genetic alterations of ADAM12 mainly included missense mutations, amplifications, and deep deletions. ADAM12 and positively correlated genes were mainly enriched in protein digestion and absorption, extracellular matrix-receptor interaction, and adhesion plaques. ADAM12 had a moderate correlation with immune cell markers EBIP1, CCNB1, EXO1, KNTC1, PRC1, and FAM198B. Prognostic model was established based on ADAM12 and immune-related genes. In vitro experiments revealed that knocking down ADAM12 inhibited cell proliferation, migration, and invasion. ADAM12 potentially plays an important role in the occurrence of LUAD and may be utilized as an immunotherapy target and a valuable prognostic biomarker for LUAD.

AGT May Serve as a Prognostic Biomarker and Correlated with Immune Infiltration in Gastric Cancer

Purpose

Angiotensinogen (AGT), as a component of the renin–angiotensin system (RAS), is associated with multiple risk factors for gastric cancer (GC). However, the relationship between AGT and tumor-infiltrating lymphocytes in GC remains elusive.

Methods

AGT expression was analyzed based on the Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier curve was employed to assess the role of AGT expression in gastric patients’ prognosis. The association between AGT expression and tumor immune infiltration was further evaluated via exploring Tumour Immune Estimation Resource (TIMER) and The Gene Expression Profiling Interactive Analysis (GEPIA). We also used multiple public databases to analyse the aberrant methylation of AGT, construct protein–protein interaction (PPI) and gene ontology (GO) analyses.

Results

AGT was overexpressed in GC tissues compared with normal gastric tissues (P<0.05). High AGT expression related with poorer overall survival of patients with GC, especially in advanced GC patients. Immune infiltration analysis revealed that AGT was associated with several immune cells (including B cells, CD4+ T cells, macrophages), and AGT expression was also associated with the markers of NK cells, TAMs, Tregs, and so on (all P<0.05). Methylation analysis indicated that hypomethylation may lead to abnormal upregulation of the AGT. GO analysis showed that AGT and its related genes were enriched in systemic arterial blood pressure by hormone, regulation of blood volume by renin-angiotensin, NIK/NF-kappaB signaling, ficolin-1-rich granule and so on.

Conclusion

AGT could act as a promising biomarker for prognosis and immune infiltration in GC.