Development of a Novel Six-miRNA-Based Model to Predict Overall Survival Among Colon Adenocarcinoma Patients

Metabolism-related lncRNAs signature to predict the prognosis of colon adenocarcinoma

BACKGROUND

Cell metabolism and long noncoding RNA (lncRNA) played crucial roles in cancer development. However, their association in colon adenocarcinoma (COAD) remains unclear.

METHODS

The COAD gene expression data and corresponding clinical data were retrieved from The Cancer Genome Atlas (TCGA) database. Differential expression of metabolic genes and lncRNA were identified by comparing tumor and normal colon tissues. Pearson correlation analysis was performed to identify metabolism-associated lncRNA. COAD patients were divided into training cohort and validation cohort by randomization. Then, a univariate Cox regression analysis was introduced to evaluate the correlations between metabolism-related lncRNAs and overall survival (OS) of the patients in the training cohort. The least absolute shrinkage and selection operator (LASSO) method was introduced to determine and establish a prognostic prediction model. Subsequently, survival analysis, receiver operating characteristic (ROC) curve analysis, and Cox regression analysis were generated to estimate the prognostic role of the LncRNA risk score in training, validation, and entire cohorts.

RESULTS

We identified 152 differentially expressed metabolism-associated lncRNAs (MRLncRNAs). A prognostic prediction model involving four metabolism-related lncRNAs were established using LASSO. In each cohort, COAD patients in the high-risk group had worse OS compared to those in the low-risk group. The ROC analyses demonstrated that the lncRNA signature performed well in predicting OS. Uni- and multivariate analysis indicated that the lncRNA signature as an independent prognostic factor. Furthermore, a correlation analysis demonstrated that LINC01138 was the most closely lncRNA related to metabolic genes. In vitro assays demonstrated that LINC01138 affects tumor progression in COAD.

CONCLUSIONS

In summary, we established a metabolism-associated lncRNAs model to predict the prognosis in COAD patients.

Transposable elements contribute to the spatiotemporal microRNA landscape in human brain development

Transposable elements (TEs) contribute to the evolution of gene regulatory networks and are dynamically expressed throughout human brain development and disease. One gene regulatory mechanism influenced by TEs is the miRNA system of post-transcriptional control. miRNA sequences frequently overlap TE loci and this miRNA expression landscape is crucial for control of gene expression in adult brain and different cellular contexts. Despite this, a thorough investigation of the spatiotemporal expression of TE-embedded miRNAs in human brain development is lacking. Here, we identify a spatiotemporally dynamic TE-embedded miRNA expression landscape between childhood and adolescent stages of human brain development. These miRNAs sometimes arise from two apposed TEs of the same subfamily, such as for L2 or MIR elements, but in the majority of cases stem from solo TEs. They give rise to in silico predicted high-confidence pre-miRNA hairpin structures, likely represent functional miRNAs, and have predicted genic targets associated with neurogenesis. TE-embedded miRNA expression is distinct in the cerebellum when compared to other brain regions, as has previously been described for gene and TE expression. Furthermore, we detect expression of previously nonannotated TE-embedded miRNAs throughout human brain development, suggestive of a previously undetected miRNA control network. Together, as with non-TE-embedded miRNAs, TE-embedded sequences give rise to spatiotemporally dynamic miRNA expression networks, the implications of which for human brain development constitute extensive avenues of future experimental research. To facilitate interactive exploration of these spatiotemporal miRNA expression dynamics, we provide the "Brain miRTExplorer" web application freely accessible for the community.

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma

Background

A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA).

Methods

From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan-Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built.

Results

Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability.

Conclusions

The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

Establishment of a 4-miRNA Prognostic Model for Risk Stratification of Patients With Pancreatic Adenocarcinoma

Pancreatic adenocarcinomas (PAADs) often remain undiagnosed until later stages, limiting treatment options and leading to poor survival. The lack of robust biomarkers complicates PAAD prognosis, and patient risk stratification remains a major challenge. To address this issue, we established a panel constructed by four miRNAs (miR-4444-2, miR-934, miR-1301 and miR-3655) based on The Cancer Genome Atlas (TCGA) and Human Cancer Metastasis Database (HCMDB) to predicted the prognosis of PAAD patients. Then, a risk prediction model of these four miRNAs was constructed by using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) regression analysis. This model stratified TCGA PAAD cohort into the low-risk and high-risk groups based on the panel-based risk score, which was significantly associated with 1-, 2-, 3-year OS (AUC=0.836, AUC=0.844, AUC=0.952, respectively). The nomogram was then established with a robust performance signature for predicting prognosis compared to clinical characteristics of pancreatic cancer (PC) patients, including age, gender and clinical stage. Moreover, two GSE data were validated the expressions of 4 miRNAs with prognosis/survival outcome in PC. In the external clinical sample validation, the high-risk group with the upregulated expressions of miR-934/miR-4444-2 and downregulated expressions of miR-1301/miR-3655 were indicated a poor prognosis. Furthermore, the cell counting kit-8 (CCK-8) assay, clone formation, transwell and wound healing assay also confirmed the promoting effect of miR-934/miR-4444-2 and the inhibiting effect of miR-1301/miR-3655 in PC cell proliferation and migration. Taken together, we identified a new 4-miRNA risk stratification model could be used in predicting prognosis in PAAD.

MiR-3682 promotes the progression of hepatocellular carcinoma (HCC) via inactivating AMPK signaling by targeting ADRA1A

INTRODUCTION AND OBJECTIVES

This study aimed to investigate miR-3682 as a biomarker in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

MiRNA and RNA profiles of 375 HCC tissues and 50 normal liver samples were downloaded from The Cancer Genome Atlas (TCGA) database. Multivariate Cox regression and Kaplan-Meier analyses were applied to examine the prognostic value of factors. Target genes of miR-3682 were analyzed by TargetScan and dual-luciferase reporter assay. Online Database for Annotation, Visualization, and Integrated Discovery (DAVID) to perform KEGG pathway enrichment. Cell counting kit-8, colony formation and migration and invasion assays were performed to analyze biological behaviors of HCC cells.

RESULTS

MiR-3682 was identified to be highly expressed in HCC tissues and cell lines. And miR-3682 was negatively and independently associated with the outcome of HCC patients. Inhibition of miR-3682 suppressed HCC cell viability and mobility. ADRA1A, predicted and confirmed as the novel target of miR-3682, was an independent and positive prognostic predictor for HCC. In addition, the knockdown of ADRA1A partially offset the inhibitory effect of miR-3682 inhibitor on the growth and mobility of HCC cells. DAVID enrichment and western blot of key signaling-related proteins analyses revealed that miR-3682 inactivated 5'-AMP-activated protein kinase (AMPK) signaling by negatively regulating ADRA1A. Mechanically, it was partially through suppressing AMPK signaling via targeting ADRA1A that miR-3682 supported the HCC cell malignant phenotype.

CONCLUSIONS

This study implicates that miR-3682 plays an oncogenetic role in HCC and can be considered a novel therapeutic target and prognostic indicator of HCC.

miR-23b-3p Inhibits the Oncogenicity of Colon Adenocarcinoma by Directly Targeting NFE2L3

Background and Aims

MicroR-23b-3p (miR-23b-3p) has been found to be abnormally expressed in a variety of malignant tumors and to play a role in tumor inhibition or promotion. However, the regulatory mechanism of miR-23b-3p in COAD remains unclear. The purpose of this study was to investigate the clinical significance of miR-23b-3p expression in COAD cells and to explore its role and regulatory mechanism in the growth of COAD.

Materials and Methods

Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure miR-23b-3p expression in COAD tissues and cell lines. After transfecting miR-23b-3p mimics into two human COAD cell lines (SW620 and LoVo), the cell counting kit-8 (CCK-8), colony formation, and 5-ethynyl-2′-deoxyuridine (EdU) assays were used to detect cell proliferation, the Transwell assay was used to measure cell migration and invasion capacity, and flow cytometry was used to evaluate cell apoptosis in vitro. In addition, a luciferase reporter assay was used to determine whether miR-23b-3p targets NFE2L3. The downstream regulatory mechanisms of miR-23b-3p action in COAD cells were also investigated. For in vivo tumorigenesis assay, COAD cells stably overexpressing miR-23b-3p were injected subcutaneously into the flank of nude mice to obtain tumors.

Results

Significantly decreased expression of miR-23b-3p was detected in COAD tissues and cell lines. Exogenous miR-23b-3p expression inhibited cell proliferation, migration, and invasion and promoted cell apoptosis of COAD cells in vitro. Nuclear factor erythroid 2 like 3 (NFE2L3) was identified as a direct target gene of miR-23b-3p. In addition, reintroduction of NFE2L3 partially abolished the anticancer effects of miR-23b-3p on COAD cells. Furthermore, miR-23b-3p overexpression hindered the growth of COAD cells in vivo.

Conclusion

miR-23b-3p inhibited the oncogenicity of COAD cells in vitro and in vivo by directly targeting NFE2L3, suggesting the importance of the miR-23b-3p/NFE2L3 pathway in the development of COAD.

Integrated Analysis of Distant Metastasis-Associated Genes and Potential Drugs in Colon Adenocarcinoma

Background: Most colon adenocarcinoma (COAD) patients die of distant metastasis, though there are some therapies for metastatic COAD. However, the genes exclusively expressed in metastatic COAD remain unclear. This study aims to identify prognosis-related genes associated with distant metastasis and develop therapeutic strategies for COAD patients.

Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA; n = 514) cohort were analyzed as a discovery dataset. Next, the data from the GEPIA database and PROGgeneV2 database were used to validate our analysis. Key genes were identified based on the differential miRNA and mRNA expression with respect to M stage. The potential drugs targeting candidate differentially expressed genes (DEGs) were also investigated.

Results: A total of 127 significantly DEGs in patients with distant metastasis compared with patients without distant metastasis were identified. Then, four prognosis-related genes (LEP, DLX2, CLSTN2, and REG3A) were selected based on clustering analysis and survival analysis. Finally, three compounds targeting the candidate DEGs, including ajmaline, TTNPB, and dydrogesterone, were predicted to be potential drugs for COAD.

Conclusions: This study revealed that distant metastasis in COAD is associated with a specific group of genes, and three existing drugs may suppress the distant metastasis of COAD.

Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD

PURPOSE

This study aims to systematically analyze multi-omics data to explore new prognosis biomarkers in colon adenocarcinoma (COAD).

MATERIALS AND METHODS

Multi-omics data of COAD and clinical information were obtained from The Cancer Genome Atlas (TCGA). Univariate Cox analysis was used to select genes which significantly related to the overall survival. GISTIC 2.0 software was used to identify significant amplification or deletion. Mutsig 2.0 software was used to identify significant mutation genes. The 9-gene signature was screened by random forest algorithm and Cox regression analysis. GSE17538 dataset was used as an external dataset to verify the predictive ability of 9-gene signature. qPCR was used to detect the expression of 9 genes in clinical specimens.

RESULTS

A total of 71 candidate genes are obtained by integrating genomic variation, mutation and prognostic data. Then, 9-gene signature was established, which includes HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, PYGO2, CTNNA1, PTPRK, and NAT1. The 9-gene signature is an independent prognostic risk factor for COAD patients. In addition, the signature shows good predicting performance and clinical practicality in training set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, and PYGO2 was increased in colon cancer tissues and the expression of CTNNA1, PTPRK, NAT1 was decreased in colon cancer tissues.

CONCLUSION

In this study, 9-gene signature is constructed as a new prognostic marker to predict the survival of COAD patients.

MiR-5683 suppresses glycolysis and proliferation through targeting pyruvate dehydrogenase kinase 4 in gastric cancer

Gastric cancer (GC) is one of the most deadly malignancies at global scale, and is particularly common in eastern Asia. MicroRNA‐5683 (miR‐5683) was confirmed to be downregulated in GC by analyzing data from the Cancer Genome Atlas. We packaged miR‐5683‐mimics and miR‐5683‐inhibitors into lentivirus vectors and transfected them into GC cells. MiR‐5683 expression and possible target genes were detected by employing quantitative real‐time polymerase chain reaction. In vitro, cell proliferation and apoptosis were analyzed using CCK‐8, colony formation assay, and flow cytometric assay. We verified the direct interaction between miR‐5683 and the possible downstream target gene pyruvate dehydrogenase kinase 4 (PDK4) through luciferase reporter assay. The role of miR‐5683 in vivo was explored by injecting stably transfected GC cells subcutaneously into nude mice. Here we show that miR‐5683 was downregulated in GC and the decreased level of miR‐5683 enhances GC cell proliferation and impairs apoptosis. Tumor oncogene PDK4, which is associated with GC overall survival and disease‐free survival, has been identified as the target gene of miR‐5683. Besides, we demonstrate that the inhibition of miR‐5683 promotes glycolysis by upregulating the PDK4 expression, thus leading to GC progression. Our study determines that miR‐5683 represses GC glycolysis and progression through targeting PDK4. MiR‐5683 overexpression may thus become a new treatment strategy for GC.