Human leukocyte antigen G (HLA-G) is a potential checkpoint molecule that plays a key role in cervical carcinogenesis. The purpose of this study was to construct and validate a prognostic risk model to predict the overall survival (OS) of cervical cancer patients, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven differentially expressed genes (DEGs) were obtained from two cervical carcinoma cell lines, namely, SiHa and HeLa, with stable overexpression of HLA-G by RNA sequencing (RNA-seq). The biological functions of these HLA-G-driven DEGs were analysed by GO enrichment and KEGG pathway using the “clusterProfiler” package. The protein-protein interactions (PPIs) were assessed using the STRING database. The prognostic relevance of each DEG was evaluated by univariate Cox regression using the TCGA-CESC dataset. After the TCGA-CESC cohort was randomly divided into training set and testing set, and a prognostic risk model was constructed by LASSO and stepwise multivariate Cox regression analysis in training set and validated in testing set or in different types of cervical cancer set. The predictive ability of the prognostic risk model or nomogram was evaluated by a series of bioinformatics methods. A total of 1108 candidate HLA-G-driven DEGs, including 391 upregulated and 717 downregulated genes, were obtained and were enriched mostly in the ErbB pathway, steroid biosynthesis, and MAPK pathway. Then, an HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. Multivariate Cox regression analysis showed that this signature is an independent risk factor for the overall survival of CESC patients. Kaplan-Meier survival analysis showed that the 5-year overall survival rate is 23.0% and 84.6% for the high-risk and low-risk patients, respectively (P<0.001). The receiver operating characteristic (ROC) curve of this prognostic model with an area under the curve (AUC) was 0.896 for 5 years, which was better than that of other clinical traits. This prognostic risk model was also successfully validated in different subtypes of cervical cancer, including the keratinizing squamous cell carcinoma, non-keratinizing squamous cell carcinoma, squamous cell neoplasms, non-squamous cell neoplasms set. Single-sample gene set enrichment (ssGSEA) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis confirmed that this signature influence tumour microenvironment and immune checkpoint blockade. A nomogram that integrated risk score, age, clinical stage, histological grade, and pathological type was then built to predict the overall survival of CESC patients and evaluated by calibration curves, AUC, concordance index (C-index) and decision curve analysis (DCA). To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.

Background: Tumor initiation and metastasis influence tumor immune exclusion and immunosuppression. Long non-coding RNA (lncRNA) LINC01614 is associated with the prognosis and metastasis of several cancers. However, the relationship between LINC01614 and cancer immune infiltration and the biofunction of LINC01614 in head and neck squamous cell carcinoma (HNSC) remain unclear.Methods: The Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets were used to analyze the expression difference and diagnostic value of LINC01614 in normal and tumor tissues. The correlation of pan-cancer prognosis and tumor stage of LINC01614 was analyzed based on the TCGA database. The pan-cancer association of LINC01614 expression with the tumor microenvironment (TME) including immune infiltration, expression of immune-related genes, and genomic instability parameters, was analyzed using the Spearman correlation method. The correlation between LINC01614 and tumor stemness evaluation indicators, RNA methylation-related genes, and drug resistance was also analyzed. The functional analysis of LINC01614 was performed using the clusterProfiler R package. The protein–protein interaction (PPI) network and ceRNA network of LINC01614 co-expressed genes and miRNA were constructed and visualized by STRING and Cytoscape, respectively. Finally, the cell location and influence of LINC01614 on cell proliferation and metastasis of HNSC cell lines were evaluated using FISH, CCK-8, wound-healing assay, and transwell assay.Results: LINC01614 was found to be overexpressed in 23 cancers and showed a highly sensitive prediction value in nine cancers (AUC >0.85). LINC01614 dysregulation was associated with tumor stage in 12 cancers and significantly influenced the survival outcomes of 26 cancer types, with only Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), uterine corpus endometrial carcinoma (UCEC), and bladder urothelial carcinoma (BLCA) showing a benign influence. LINC01614 was also associated with immune cell infiltration, tumor heterogeneity, cancer stemness, RNA methylation modification, and drug resistance. The potential biological function of LINC01614 was verified in HNSC, and it was found to play important roles in proliferation, immune infiltration, immunotherapy response, and metastasis of HNSC.Conclusion: LINC01614 may serve as a cancer diagnosis and prognosis biomarker and an immunotherapy target for specific cancers.

Epithelial cell transformation (EMT) plays an important role in the pathogenesis and metastasis of hepatocellular carcinoma (HCC). We aimed to establish a genetic risk model to evaluate HCC prognosis based on the expression levels of EMT-related genes. The data of HCC patients were collected from TCGA and ICGC databases. Gene expression differential analysis, univariate analysis, and lasso combined with stepwise Cox regression were used to construct the prognostic model. Kaplan-Meier curve, receiver operating characteristic (ROC) curve, calibration analysis, Harrell's concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the predictive ability of the risk model or nomogram. GO and KEGG were used to analyze differently expressed EMT genes, or genes that directly or indirectly interact with the risk-associated genes. A 10-gene signature, including TSC2, ACTA2, SLC2A1, PGF, MYCN, PIK3R1, EOMES, BDNF, ZNF746, and TFDP3, was identified. Kaplan-Meier survival analysis showed a significant prognostic difference between high- and low-risk groups of patients. ROC curve analysis showed that the risk score model could effectively predict the 1-, 3-, and 5-year overall survival rates of patients with HCC. The nomogram showed a stronger predictive effect than clinical indicators. C-index, DCA, and calibration analysis demonstrated that the risk score and nomogram had high accuracy. The single sample gene set enrichment analysis results confirmed significant differences in the types of infiltrating immune cells between patients in the high- and low-risk groups. This study established a new prediction model of risk gene signature for predicting prognosis in patients with HCC, and provides a new molecular tool for the clinical evaluation of HCC prognosis.

Purpose. To elucidate the clinical and prognostic role of PDZ and LIM domain protein (PDLIM) genes and the association to epithelial-mesenchymal transition (EMT) and immune cell infiltration in patients with prostate cancer (PRAD). Methods. The data of RNA-seq, DNA methylation, and clinical features of PRAD patients were collected from The Cancer Genome Atlas (TCGA) database to define the prognostic value of PDLIM gene expression and the association with EMT and immune cell infiltration. A tissue microarray including 134 radical prostatectomy specimens was served as validation by immunohistochemistry (IHC) staining analysis. Results. The mRNA levels of PDLIM1/2/3/4/6/7 were significantly downregulated, while PDLIM5 was upregulated in PRAD ( ). High expression of PDLIM2 mRNA suggests poor progression free interval in PRAD patients. DNA methylation of PDLIM2 was correlated with its mRNA expression level, and that the cg22973076 methylation site in PDLIM2 was associated with shorter PFI ( ) in PRAD. Single-sample gene-set enrichment and gene functional enrichment results showed that PDLIM2 was correlated with EMT and immune processes. Spearman’s test showed a significant correlation with six reported EMT signatures and several EMT signature-related genes. Tumor microenvironment analysis revealed that the PDLIM2 mRNA expression was positively correlated with the immune score, stromal score, and various tumor infiltrating immune cells. Additionally, the results showed that patients in the high-PDLIM2 mRNA expression group may be more sensitive to immune checkpoint blockade therapy. Finally, IHC analysis further implicated the protein level of PDLIM2 was upregulated in PRAD and acts as a novel potential biomarker in predicting tumor progression. Conclusion. Our study suggests that PDLIM family genes might be significantly correlated with oncogenesis and the progression of PRAD. PDLIM2 correlated with EMT and immune cell infiltration by acting as an oncogene in PRAD, which may serve as a potential prognostic biomarker for PRAD patients.

Objectives: Nectins are a new class of cell-adhesion molecules that play an important role in tumorigenesis and disease progression. The aim of this study was to investigate the prognostic and pathogenetic roles of nectins in hepatocellular carcinoma (HCC).Methods: The expression levels of the nectin family in HCC and their role in prognosis were analyzed by bioinformatics analysis based on The Cancer Genome Atlas (TCGA) liver hepatocellular carcinoma database. The correlations between nectins and immune cells were analyzed using TIMER. The functional enrichment of the nectin-1 coexpression network was evaluated in TCGA cohort, and the expression levels of nectin-1 were detected by immunohistochemistry and Western blot analysis. A Transwell kit was used for cell migration experiments. Cell proliferation was analyzed using Cell Counting Kit-8.Results: The expression levels of nectin-1 protein in the cancer tissues of 28 patients with HCC were higher than those in paracancerous tissues. The Kaplan–Meier plotter analysis showed that the high expression of all nectin family numbers was related to the poor prognosis of HCC patients. The abnormal expression of nectin-1 effectively distinguished the prognosis at different stages and grades of HCC. The high expression of 17 methylation sites of the nectin-1 gene was related to the high overall survival of HCC patients. Kyoto Encyclopedia of Genes and Genomes analysis of genes negatively correlated with nectin-1, revealing their close relation to the regulation of the immune-effector process. Pearson’s correlation analysis showed that nectin-1 was significantly positively correlated with multiple immune genes and B cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cell infiltration. Cell proliferation of the knockdown (KD) group decreased significantly compared to the NC-KD group. The number of metastatic cells in the KD group decreased significantly compared to that in the NC-KD group.Conclusions: Abnormal expression of nectins and multiple methylation sites closely correlates with poor prognosis in HCC patients. Nectins are related to immune cell infiltration and immune-related genes. In particular, nectin-1 can promote the proliferation and migration of liver cancer cells and distinguish the prognosis at different stages and grades of HCC. Nectin-1 might be a new potential molecular marker for prognostic evaluation and also a therapeutic target for HCC.

Background: Hepatocellular carcinoma (HCC) is the second malignancy worldwide. POLA2 initiates DNA replication, regulates cell cycle and gene repair that promote tumorigenesis and disease progression. However, the prognostic and biological function roles of POLA2 in HCC had not been conclusively determined. Methods: The expression levels and prognosis role of POLA1 and POLA2 in HCC were analyzed based on TCGA-LIHC database and recruited 24 HCC patients. Gene mutations were analyzed using “maftools” package. POLA2 and immune cells correlations were analyzed by TIMER. POLA2 co-expressed genes functional enrichment were evaluated using Metascape. The mRNA and protein level of POLA2 was detected in HCC cells and tissues. Cell migration, invasion, proliferation, cell cycle and HCC cell lines derived xenograft model were performed to investigate POLA2 biological function. Results: POLA2 was significantly high expressed in HCC than in normal liver tissue in both TCGA-LIHC and our collected HCC samples. In validation cohort, POLA2 significantly related to tumor differentiation, tumor size and Ki-67 (p < 0.05). In TCGA-LIHC cohort, overexpression of POLA2 predicted a low OS and associated with different clinical stages. Multivariate Cox regression showed overexpression of POLA2 effectively distinguished the prognosis at different T, N, M, stages and grades of HCC. POLA2 expression correlated with mutation burden, immune cells infiltration and immune-associated genes expression of HCC. Functional enrichment revealed that POLA2 co-expressed genes were linked to cellular activity, plasma membrane protein complex and leukocyte activity, immune response-regulated cell surface receptor signaling pathway, and immune response-regulated signaling pathway. Moreover, POLA2 was also positively co-expressed with some immune checkpoints (CD274, CTL-4, HAVCR2, PDCD1, PDCD1LG2, TIGIT, and LAG3) (p < 0.001). Gene knockdown revealed that POLA2 promoted proliferation, migration, invasion, and cell cycle of SMMC-7721 and HepG2. The HCC xenograft tumor model also demonstrated remarkably tumor size inhibition, tumor proliferation inhibtion and tumor necrosis promotion when POLA2 knockdown. Conclusions: POLA2 influenced immune microenvironment and tumor progression of HCC indicated that it might be a potential molecular marker for prognostic evaluation or a therapeutic target for HCC.