Construction and validation of a prognostic model based on autophagy-related genes for hepatocellular carcinoma in the Asian population

BACKGROUND AND OBJECTIVE

Hepatocellular carcinoma (HCC), which has a complex pathogenesis and poor prognosis, is one of the most common malignancies worldwide. Hepatitis virus B infection is the most common cause of HCC in Asian patients. Autophagy is the process of digestion and degradation, and studies have shown that autophagy-associated effects are closely related to the development of HCC. In this study, we aimed to construct a prognostic model based on autophagy-related genes (ARGs) for the Asian HCC population to provide new ideas for the clinical management of HCC in the Asian population.

METHODS

The clinical information and transcriptome data of Asian patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) database, and 206 ARGs were downloaded from the human autophagy database (HADB). We performed differential and Cox regression analyses to construct a risk score model. The accuracy of the model was validated by using the Kaplan-Meier (K-M) survival curve, receiver operating characteristic (ROC) curve, and univariate and multivariate Cox independent prognostic analyses. The results Thirteen ARGs that were significantly associated with prognosis were finally identified by univariate and multivariate Cox regression analyses. The K-M survival curves showed that the survival rate of the low-risk group was significantly higher than that of the high-risk group (p < 0.001), and the multi-indicator ROC curves further demonstrated the predictive ability of the model (AUC = 0.877).

CONCLUSION

The risk score model based on ARGs was effective in predicting the prognosis of Asian patients with HCC.

A Comprehensive Prognostic Analysis of POLD1 in Hepatocellular Carcinoma

Background

DNA polymerase delta 1 catalytic subunit (POLD1) plays a key role in DNA replication and damage repair. A defective DNA proofreading function caused by POLD1 mutation contributes to carcinogenesis, while POLD1 overexpression predicts poor prognosis in cancers. However, the effect of POLD1 in hepatocellular carcinoma (HCC) is not well-understood.

Methods

Expression patterns of POLD1 were evaluated in TCGA and the HPA databases. Kaplan-Meier curves and Cox regression were used to examine the prognostic value of POLD1. The prognostic and predictive value of POLD1 was further validated by another independent cohort from ICGC database. The influences of DNA copy number variation, methylation and miRNA on POLD1 mRNA expression were examined. The correlation between infiltrating immune cells and POLD1 expression was analyzed. GO and KEGG enrichment analyses were performed to detect biological pathways associated with POLD1 expression in HCC.

Results

POLD1 was overexpressed in HCC (n = 369) compared with adjacent normal liver (n = 50). POLD1 upregulation was significantly correlated with positive serum AFP and advanced TNM stage. Kaplan–Meier and multivariate analyses suggested that POLD1 overexpression predicts poor prognosis in HCC. DNA copy gain, low POLD1 methylation, and miR‑139-3p downregulation were associated with POLD1 overexpression. Besides, POLD1 expression was associated with the infiltration levels of dendritic cell, macrophage, B cell, and CD4 + T cell in HCC. Functional enrichment analysis suggested “DNA replication”, “mismatch repair” and “cell cycle” pathways might be involved in the effect of POLD1 on HCC pathogenesis. Additionally, POLD1 mRNA expression was significantly associated with tumor mutation burden, microsatellite instability, and prognosis in various tumors.

Conclusions

POLD1 may be a potential prognostic marker and promising therapeutic target in HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09284-y.

Identification of Transcriptional Pattern Related to Immune Cell Infiltration With Gene Co-Expression Network in Papillary Thyroid Cancer

BACKGROUND

A growing body of evidence suggests that immune cell infiltration in cancer is closely related to clinical outcomes. However, there is still a lack of research on papillary thyroid cancer (PTC).

METHODS

Based on single-sample gene set enrichment analysis (SSGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) tool, the infiltration level of immune cell and key modules and genes associated with the level of immune cell infiltration were identified using PTC gene expression data from The Cancer Genome Atlas (TCGA) database. In addition, the co-expression network and protein-protein interactions network analysis were used to identify the hub genes. Moreover, the immunological and clinical characteristics of these hub genes were verified in TCGA and GSE35570 datasets and quantitative real-time polymerase chain reaction (PCR). Finally, receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of hub genes.

RESULTS

Activated B cell, activated dendritic cell, CD56bright natural killer cell, CD56dim natural killer cell, Eosinophil, Gamma delta T cell, Immature dendritic cell, Macrophage, Mast cell, Monocyte, Natural killer cell, Neutrophil and Type 17 T helper cell were significantly changed between PTC and adjacent normal groups. WGCNA results showed that the black model had the highest correlation with the infiltration level of activated dendritic cells. We found 14 hub genes whose expression correlated to the infiltration level of activated dendritic cells in both TCGA and GSE35570 datasets. After validation in the TCGA dataset, the expression level of only 5 genes (C1QA, HCK, HLA-DRA, ITGB2 and TYROBP) in 14 hub genes were differentially expressed between PTC and adjacent normal groups. Meanwhile, the expression levels of these 5 hub genes were successfully validated in GSE35570 dataset. Quantitative real-time PCR results showed the expression of these 4 hub genes (except C1QA) was consistent with the results in TCGA and GSE35570 dataset. Finally, these 4 hub genes had diagnostic value to distinguish PTC and adjacent normal controls.

CONCLUSIONS

HCK, HLA-DRA, ITGB2 and TYROBP may be key diagnostic biomarkers and immunotherapy targets in PTC.

The Early Diagnostic and Prognostic Value of BIRC5 in Clear-Cell Renal Cell Carcinoma Based on the Cancer Genome Atlas Data

PURPOSE

The aim of this study was to investigate the role of BIRC5 for early diagnosis and prognosis in clear-cell renal cell carcinoma (ccRCC) by studying the expression of BIRC5 and the correlation between BIRC5 expression and clinicopathological parameters and prognosis in ccRCC.

METHODS

The BIRC5 expression in ccRCC tissues and normal kidney tissues was measured using the Cancer Genome Atlas database and the Human Protein Atlas database. The correlation between BIRC5 expression and clinicopathological parameters and prognosis in ccRCC was analyzed using UALCAN, the Kaplan-Meier plotter, GEPIA, and SurvExpress. Thirteen-paired ccRCC plasma samples were used to verify the BIRC5 early diagnosis value of ccRCC.

RESULTS

The BIRC5 expression is significantly higher in ccRCC than in normal kidney tissues, and is correlated with the clinical stage and pathological grade of ccRCC (p < 0.05). The result of analyzing the relationship between BIRC5 expression and outcomes in ccRCC indicates that a high BIRC5 expression is an independent prognostic factor affecting the overall survival and disease-free survival of ccRCC (p < 0.05). Compared with normal kidney tissues, the immunohistochemical test shows that BIRC5 is significantly upregulated in ccRCC tissues. mRNA expression levels of BIRC5 were significantly higher in the ccRCC plasma than normal (p < 0.05).

CONCLUSIONS

The high expression of BIRC5 is an important indicator for the prognosis of ccRCC, which makes BIRC5 an effective biomarker for predicting the prognosis of patients in ccRCC. BIRC5 may be a great potential biomarker for early diagnosis of ccRCC.

CDKN3 expression predicates poor prognosis and regulates adriamycin sensitivity in hepatocellular carcinoma in vitro

Objective

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related deaths worldwide. This study investigated the relationship between cyclin-dependent kinase inhibitor (CDKN)3 and prognosis and pathological characteristics in HCC patients to determine whether it could be used as a prognostic factor and/or therapeutic target for HCC drug development.

Methods

We previously showed that CDKN3 is deregulated in HCC tumor samples. Here, bioinformatics analysis was used to assess the relationship between CDKN3 gene expression and the characteristics of HCC patients from Gene Expression Omnibus and The Cancer Genome Atlas databases. Additionally, CDKN3 expression was silenced by small interfering RNA to determine its effect on HCC cell proliferation and on HCC cell sensitivity to adriamycin chemotherapy.

Results

Bioinformatics analysis showed a negative correlation between CDKN3 expression and both disease-free survival and overall survival. CDKN3 silencing did not significantly suppress the proliferation of HCC cells, but did decrease their sensitivity to adriamycin.

Conclusions

CDKN3 may have a dual role during the development of HCC, and could be used as an independent prognostic factor and therapeutic target for HCC treatment.

SEC61G is upregulated and required for tumor progression in human kidney cancer

Kidney cancer is a malignant tumor of the urinary system. Although the 5-year survival rate of patients with kidney cancer has increased by ~30% in recent years due to the early detection of low-grade tumors using more accurate diagnostic methods, the global incidence of kidney cancer continues to increase every year. Therefore, identification of novel and efficient candidate genes for predicting the prognosis of patients with kidney cancer is important. The present study aimed to investigate the role of SEC61 translocon subunit-γ (SEC61G) in kidney cancer. The Cancer Genome Atlas database was screened to obtain the expression profile of SEC61G and identify its association with kidney cancer prognosis. Furthermore, the in vitro effect of SEC61G knockdown on kidney cancer cell proliferation, migration, invasion and apoptosis was investigated using a Cell Counting Kit-8 assay, wound healing assay, Transwell assay and flow cytometry. The results demonstrated that compared with healthy tissues, SEC61G was upregulated in human kidney tumor tissues, which was associated with poor prognosis. In addition, SEC61G knockdown significantly inhibited kidney cancer cell proliferation, migration and invasion compared with the negative control (NC) group. Furthermore, E-cadherin expression was significantly upregulated, and N-cadherin and β-catenin expression levels were significantly downregulated in SEC61G-knockdown kidney cancer cells compared with the NC group. In addition, compared with the NC group, SEC61G knockdown significantly promoted cell apoptosis in a caspase-dependent manner. The aforementioned results suggested that SEC61G might serve as a proto-oncogene to promote kidney tumor progression. Therefore, the present study provided a novel candidate gene for predicting the prognosis of patients with kidney cancer.

Identification of key genes controlling cancer stem cell characteristics in gastric cancer

BACKGROUND

Self-renewal of gastric cancer stem cells (GCSCs) is considered to be the underlying cause of the metastasis, drug resistance, and recurrence of gastric cancer (GC).

AIM

To characterize the expression of stem cell-related genes in GC.

METHODS

RNA sequencing results and clinical data for gastric adenoma and adenocarcinoma samples were obtained from The Cancer Genome Atlas database, and the results of the GC mRNA expression-based stemness index (mRNAsi) were analyzed. Weighted gene coexpression network analysis was then used to find modules of interest and their key genes. Survival analysis of key genes was performed using the online tool Kaplan-Meier Plotter, and the online database Oncomine was used to assess the expression of key genes in GC.

RESULTS

mRNAsi was significantly upregulated in GC tissues compared to normal gastric tissues (P < 0.0001). A total of 16 modules were obtained from the gene coexpression network; the brown module was most positively correlated with mRNAsi. Sixteen key genes (BUB1, BUB1B, NCAPH, KIF14, RACGAP1, RAD54L, TPX2, KIF15, KIF18B, CENPF, TTK, KIF4A, SGOL2, PLK4, XRCC2, and C1orf112) were identified in the brown module. The functional and pathway enrichment analyses showed that the key genes were significantly enriched in the spindle cellular component, the sister chromatid segregation biological process, the motor activity molecular function, and the cell cycle and homologous recombination pathways. Survival analysis and Oncomine analysis revealed that the prognosis of patients with GC and the expression of three genes (RAD54L, TPX2, and XRCC2) were consistently related.

CONCLUSION

Sixteen key genes are primarily associated with stem cell self-renewal and cell proliferation characteristics. RAD54L, TPX2, and XRCC2 are the most likely therapeutic targets for inhibiting the stemness characteristics of GC cells.

Identification of two molecular subtypes of dysregulated immune lncRNAs in ovarian cancer

Long non-coding RNA (lncRNA) has increasingly been identified as a key regulator in pathologies such as cancer. Multiple platforms were used for comprehensive analysis of ovarian cancer to identify molecular subgroups. However, lncRNA and its role in mapping the ovarian cancer subpopulation are still largely unknown. RNA-sequencing and clinical characteristics of ovarian cancer were acquired from The Cancer Genome Atlas database (TCGA). A total of 52 lncRNAs were identified as aberrant immune lncRNAs specific to ovarian cancer. We redefined two different molecular subtypes, C1(188) and C2(184 samples), in "iClusterPlus" R package, among which C2 grouped ovarian cancer samples have higher survival probability and longer median survival time (P <0.05) with activated IFN-gamma response, Wound Healing and Cytotoxic lymphocytes signal; 456 differentially expressed genes were acquired in C1 and C2 subtypes using limma (3.40.6) package, among which 419 were up-regulated and 37 were down-regulated, in TCGA dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis revealed that these genes were actively involved in ECM-receptor interaction, PI3K-Akt signaling pathway interaction KEGG pathway. Compared with the existing immune subtype, the Cluster2 sample showed a substantial increase in the proportion of the existing C2 immune subtype, accounting for 81.37%, which was associated with good prognosis. Our C1 subtype contains only 56.49% of the existing immune C1 and C4, which also explains the poor prognosis of C1. Furthermore, 52 immune-related lncRNAs were used to divide the TCGA-endometrial cancer and cervical cancer samples into two categories, and C2 had a good prognosis. The differentially expressed genes were highly correlated with immune-cell-related pathways. Based on lncRNA, two molecular subtypes of ovarian cancer were identified and had significant prognostic differences and immunological characteristics.

Identifying prognostic biomarkers in endometrial carcinoma based on ceRNA network

PURPOSE

Endometrial carcinoma (EC), a common gynecological malignancy with high incidence, affects the mental and physical health of women. Mounting evidence shows that long noncoding RNAs (lncRNAs), messenger RNAs (mRNAs), and microRNAs (miRNAs) have instrumental roles in various biological processes associated with the pathogenesis of EC. In this research, we intend to further study the mechanism of EC and the potential predictive markers of EC.

METHODS

First, we obtained original data of EC RNA transcripts from The Cancer Genome Atlas database and performed differential analysis. Subsequently, according to the miRcode online software, relationship pairs of lncRNA-miRNA were constructed, and miRNA-mRNA pairs were established based on miRDB, TargetScan, and miRTarBase. Then, we constructed the competing endogenous RNA (ceRNA) network based on lncRNA-miRNA and miRNA-mRNA pairs. To further explain the function of the ceRNA network and explore the potential prognostic markers, functional enrichment analysis, and survival analysis were carried out.

RESULTS

The research showed that there were 744 differential expression lncRNAs (DElncRNAs), 164 differential expression miRNAs (DEmiRNAs), and 2447 differential expression mRNAs (DEmRNAs) between EC tissues and normal tissues. Subsequently, we built 103 DEmiRNA-DEmRNA interaction pairs and 369 DElncRNA-DEmiRNA pairs. Then, we established the ceRNA network of EC, including 62 DElncRNAs, 26 DEmiRNAs, and 70 DEmRNAs. Moreover, 10 of 62 lncRNAs, 19 of 70 mRNAs, and 4 of 26 miRNAs that closely related to the survival of EC with P < .05 were obtained. Notably, based on this network, it was found that LINC00261-hsa-mir-31 pair and LINC00261-hsa-mir-211 target pairs could be used as the potential prognostic markers of EC.

CONCLUSION

This research recommended an available basis for the molecular mechanism of EC and prognosis prediction, which could help guide the subsequent treatments and predict the prognosis for patients with EC.

A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma

Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non-coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis-related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood-based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk-score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high-/low-risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1. Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer.