Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma

Impact of MIR137HG rs7554283 on susceptibility to high-altitude pulmonary edema in the Chinese population

Aim: MIR137 host gene (MIR137HG) variants were involved in a variety of diseases, but its role in high-altitude pulmonary edema (HAPE) has not been reported. The study aimed to study the association between MIR137HG single-nucleotide polymorphisms and HAPE risk in the Chinese population.Materials & methods: Based on the Plink software, odds ratio and 95% confidence interval were used for logistic regression analysis to evaluate the association between MIR137HG polymorphisms and the risk of HAPE.Results: We discovered that MIR137HG rs7554283 was associated with a reduced risk of HAPE. In both individuals older than 32 years and those younger than 32 years, we observed that rs7554283 was associated with a decreased risk of HAPE.Conclusion: In conclusion, MIR137HG rs7554283 may be related to a reduced susceptibility to HAPE in the Chinese population. These results provide a theoretical basis for the role of MIR137HG single-nucleotide polymorphisms in the occurrence of HAPE.

LncRNA PITPNA-AS1 mediates the diagnostic potential of miR-129-5p in prostate cancer

BACKGROUND

LncRNA has an effective value in many diseases, which has long been applied in the diagnosis, treatment and prognosis of prostate cancer. This study focused on lncRNA PITPNA-AS1, and its diagnostic potential in prostate cancer has been explored.

METHODS

The expression of PITPNA-AS1 and miR-129-5p in prostate cancer serum and sample cells was determined by real-time quantitative polymerase chain reaction (RT-qPCR). The relationship between the expression of PITPNA-AS1 and clinicopathological parameters was considered. ROC curve prompted the diagnostic value of PITPNA-AS1. The effect of PITPNA-AS1 on prostate cancer cells was verified using vitro cells assay. Luciferase activity assay and RIP assay demonstrated the sponge relationship of PITPNA-AS1 to miR-129-5p.

RESULTS

PITPNA-AS1 level was increased, while miR-129-5p was obviously decreased in prostate cancer. PITPNA-AS1 expression was associated with Gleason grade, lymph node metastasis and TNM stage in patients. The area under the curve (AUC) was 0.910, with high sensitivity and specificity. PITPNA-AS1 was elucidated to directly target miR-129-5p, whereas silencing PITPNA-AS1 negatively affected prostate cancer cell proliferation, migration and invasion. Intervention of miR-129-5p inhibitor reversed the effect of silencing PITPNA-AS1 on cells.

CONCLUSIONS

PITPNA-AS1 was relatively highly expressed in prostate cancer and mediated the pathophysiological process of patients, which may serve as a diagnostic indicator. Silencing of the PITPNA-AS1 sponge miR-129-5p inhibited the biological function of the cells, indicating that PITPNA-AS1 may represent a novel therapeutic target for prostate cancer.

How CLSPN could demystify its prognostic value and potential molecular mechanism for hepatocellular carcinoma: A crosstalk study

BACKGROUND & AIMS

CLSPN, a critical component of the S-phase checkpoint in response to DNA replication stress, has been implicated in the pathogenesis of multiple tumor types. The rising incidence of hepatocellular carcinoma (HCC) poses a significant challenge to global public health. Despite this, the specific functions of CLSPN in the development of HCC remain poorly understood.

METHODS

We systematically evaluated the expression of CLSPN, prognosis and immune infiltration in patients with HCC and identified a competing endogenous RNA (ceRNA) network by using public database. The RT-qPCR, western blot, CCK8, transwell, flow cytometry, animal experiments, proteasome inhibition experiment, Co-IP assay and mass spectrometry were applied to explore its biological functions, post-transcriptional modifications and potential molecular mechanisms of CLSPN in HCC.

RESULTS

We verified the expression of CLSPN, and its high expression is an independent prognostic factor in HCC. The expression of CLSPN is also associated with the immune microenvironment of HCC. CLSPN silencing inhibited the proliferation, migration, invasion and cell cycle progression of HCC cells. We established a PSMA3-AS1/hsa-miR-101-3p/CLSPN regulator axis in HCC. CLSPN was influenced by ubiquitination and was involved in the Wnt/β-catenin pathway to regulate HCC progression.

CONCLUSIONS

It was the first time to comprehensively discover and identify the expression, prognosis, immunotherapy, RNAs regulator, posttranscriptional modification, and molecular mechanisms of CLSPN in HCC. These novel insights have the potential to expedite the development of personalized treatment strategies and translational medicine approaches for HCC patients.

Noval ceRNA axis-mediated high expression of TOP2A correlates with poor prognosis and tumor immune infiltration of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a highly malignant tumor with limited treatment options, suboptimal efficacy, and poor prognosis, resulting in an economic burden to countries worldwide. TOP2A is a mammalian protein that plays a vital role in DNA replication. Previous studies have shown that upregulation of TOP2A expression is associated with tumorigenesis and progression in various cancers, but the exact mechanism of upregulation remains unclear.

Methods

We first conducted a pan-cancer analysis using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases to study the oncogenicity of TOP2A through the cBioPortal database. Next, using The Encyclopedia of RNA Interactomes (ENCORI) database, we identified microRNAs (miRNAs) that are associated with the downregulation of TOP2A and investigated potential long non-coding RNAs (lncRNAs) that may act as competing endogenous RNAs (ceRNAs) by binding to candidate miRNAs. We then analyzed immune cell infiltration and immune checkpoints using the TIMER database. Finally, we performed a multivariate regression analysis using lncRNAs and clinical pathological characteristics, constructed a nomogram to predict the prognosis of HCC based on the analysis results, and evaluated its diagnostic efficiency.

Results

TOP2A was highly expressed in HCC and was associated with poor patient prognosis. TOP2A was subject to post-transcriptional regulation in HCC, with the ceRNA mechanism being a significant pathway. miR-139-5p was an important miRNA that suppressed the upregulation of TOP2A in HCC, and patients with low expression of miR-139-5p had worse overall survival (OS). After screening and analysis, three lncRNAs, AC078846.1, AC124798.1 and SNHG3, were found to inhibit the activity of miR-139-5p through the ceRNA mechanism, and patients with high expression of these three lncRNAs had worse prognosis. In addition, TOP2A was found to be closely related to tumor-infiltrating immune cells (TIICs) and immune checkpoints. A nomogram constructed using the three lncRNAs and selected clinicopathological features showed good predictive value for the prognosis of liver cancer.

Conclusions

The TOP2A-miR-139-5p-AC078846.1/AC124798.1/SNHG3 axis plays a significant role in the progression of HCC and leads to poor patient outcomes. Additionally, TOP2A influences the development of HCC by affecting TIICs and immune checkpoints. A nomogram constructed using the three lncRNAs and clinicopathological features has good clinical utility.

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignant tumor with high recurrence and mortality rates. This study aimed to develop and validate a prognostic model for patients with UCEC based on cuproptosis-related long non-coding RNA (lncRNA) signature.

Methods

Transcriptome and clinical UCEC data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis was conducted to screen out the cuproptosis-related lncRNAs, and univariate regression analysis was performed to determine prognostic factors associated with overall survival (OS). A cuproptosis-related lncRNA risk model was constructed through least absolute shrinkage and selection operator (LASSO) regression and cross-validation. The accuracy and reliability of the model were verified through Kaplan-Meier (KM), proportional hazards model (Cox) regression, nomogram, principal component analysis (PCA), and stage analysis. Gene Ontology (GO) enrichment, immune function, and tumor mutation burden (TMB) analyses were conducted between low-risk and high-risk groups, and antineoplastic drugs were predicted.

Results

By correlation analysis, 155 cuproptosis-related lncRNAs were acquired, and 9 lncRNAs were identified as independent prognostic factors. A 6-cuproptosis-related lncRNA model was established. The results revealed that patients in the high-risk group were more inclined to have a poor OS than those in the low-risk group. Risk score was an independent prognostic factor and had a high accuracy and predictive value. The extracellular structure and anchored components of membrane-related GO terms were significantly enriched. Immune function and TMB results were assumed to be different from each other, which might explain a better outcome in the low-risk group than that in the high-risk group. Eighteen compounds were predicted as chemotherapy drugs with high half maximal inhibitory concentration (IC50) in the high-risk group.

Conclusions

We successfully developed a cuproptosis-related lncRNA risk model for the prediction of prognosis, while simultaneously providing insights on new approaches for immunotherapy and chemotherapy for patients with UCEC.

A Platinum Resistance-Related lncRNA Signature for Risk Classification and Prognosis Prediction in Patients with Serous Ovarian Cancer

Accurate risk stratification for patients with serous ovarian cancer (SOC) is pivotal for treatment decisions. In this study, we identified a lncRNA-based signature for predicting platinum resistance and prognosis stratification for SOC patients. We analyzed the RNA-sequencing data and the relevant clinical information of 295 SOC samples obtained from The Cancer Genome Atlas (TCGA) database and 180 normal ovarian tissues from the Genotype-Tissue Expression (GTEx) database. A total of 284 differentially expressed lncRNAs were screened out between platinum-sensitive and platinum-resistant groups by univariate Cox regression analysis. Then, a signature consisting of eight prognostic lncRNAs was used to construct a lncRNA score model by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The ROC analysis showed that this signature had a good predictive performance for chemotherapy response in the training set (AUC = 0.8524) and the testing and whole sets with 0.8142 and 0.8393 of AUC, respectively. Dichotomized by the risk score of lncRNAs (lncScore), the high-risk patients showed significantly shorter progression-free survival (PFS) and overall survival (OS). Based on the final Cox model, a nomogram comprising the 8-lncRNA signature and 3 clinicopathological risk factors was then established for clinical application to predict the 1, 2, and 3-year PFS of SOC patients. The gene set enrichment analysis (GSEA) revealed that genes in the high-risk group were active in ATP synthesis, coupled electron transport, and mitochondrial respiratory chain complex assembly. Overall, our findings demonstrated the potential clinical significance of the 8-lncRNA-based classifier as a novel biomarker for outcome prediction and therapy decisions in SOC patients with platinum treatment.

Construction of novel lncRNA-miRNA-mRNA ceRNA networks associated with prognosis of hepatitis C virus related hepatocellular carcinoma

Background

Hepatitis C virus (HCV) infection contribute to liver fibrosis and cirrhosis, which significantly increases the risk of hepatocellular carcinoma (HCC) development. Previous studies have demonstrated the pivotal role of competitive endogenous RNA (ceRNA) networks in tumorigenesis and cancer progression. Consequently, we herein seek to identify and evaluate the prognostic relevance of a novel ceRNA network associated with HCV-related HCC.

Methods

Differentially expressed genes (DEGs) in GSE140846 dataset from GEO were identified using Network Analyst, and GO, KEGG and Reactome analyses were performed. Furthermore, a protein-protein interaction network was generated, and hub genes were detected. Hub gene expression levels, as well as those of their upstream lncRNAs and miRNAs and associated survival analyses were conducted using appropriate bioinformatics databases. Predicted target relationships were used to establish putative ceRNA networks for HCV-related HCC.

Results

A total of 372 and 360 up- and down-regulated DE-mRNA were identified, which were associated with nuclear division, cell cycle, and ATPase activity. A PPI network containing 704 DE-mRNAs was constructed, and the 6 hub gene with the highest degree of connectivity were selected for subsequent analysis. We discovered that 22 miRNAs and 4 lncRNAs upstream of 11 hub gene were significantly associated with poor prognosis of HCV-related HCC, and used them to constructe a prognostic ceRNA network. Further experiments confirmed the ceRNA-regulatory relationship of BUB1-hsa-miR-193a-3p-MALAT1.

Conclusion

This study provides novel insights into the lncRNA-miRNA-mRNA ceRNA network, and reveals potential lncRNA biomarkers in HCV related HCC.

Identification of Gefitinib Resistance-Related lncRNA-miRNA-mRNA Regulatory Networks and Corresponding Prognostic Signature in Patients with Lung Adenocarcinoma

Purpose

To identify and characterize gefitinib resistance-related (GefR-related) lncRNAs and construct a prediction model for lung adenocarcinoma (LUAD).

Methods

Differential expression analysis between PC9 and gefitinib-resistant PC9 (PC9GR) cell samples was performed to screen GefR-related lncRNAs and mRNAs based on the GSE34228 dataset. These lncRNAs, mRNAs, and their corresponding microRNAs (miRNAs) were used to construct the GefR-related network and PPI networks. Functional enrichment analyses were conducted using the STRING database. A prognostic signature was developed using the TCGA dataset. The reliability of the signature was tested using the Kaplan–Meier method and ROC curve. Lastly, the FZD4-associated ceRNA subnetwork was selected to confirm the in vitro expressions of the GefR-related lncRNAs using RT-qPCR assay.

Results

A GefR-related ceRNA network that consists of 35 miRNAs, 26 lncRNAs, and 179 mRNAs was constructed. Then, 20 hub genes were screened from the targeted mRNAs of the constructed PPI network, and enrichment analysis identified relevant enriched pathways. We also constructed a prognostic signature for LUAD based on nine mRNAs in the GefR-related ceRNA network. The 9-mRNA signature was an independent predictor of LUAD, the AUC produced by ROC analysis showed a good predictive power of the model, and Kaplan–Meier analysis showed poorer outcomes in the high-risk group, relative to the low-risk group. Lastly, MIR137HG and ZNF295-AS1 levels were found to be associated with gefitinib resistance and exerted their functions through the ceRNA mechanism.

Conclusion

We established a prognostic signature and identified two lncRNAs (MIR137HG and ZNF295-AS1) with potential significant roles in gefitinib resistance.

Integrated Analysis of ceRNA Network to Reveal Potential Prognostic Biomarkers for Glioblastoma

Glioblastoma (GBM), originating in the brain, is a universally aggressive malignant tumor with a particularly poor prognosis. Therefore, insight into the critical role of underlying genetic mechanisms is essential to developing new therapeutic approaches. This study aims to identify potential markers with clinical and prognostic significance in GBM. To this end, increasing numbers of differentially expressed RNA have been identified used to construct competitive endogenous RNA networks for prognostic analysis via comparison and analysis of RNA expression levels of tumor and normal tissues in glioblastoma. This analysis demonstrated that the RNA expression patterns of normal and tumor samples were significantly different. Thus, the resulting differentially expressed RNAs were used to construct competitive endogenous RNA (competing endogenous RNA, ceRNA) networks. The functional enrichment indicated mRNAs in the network are critically involved in a variety of biological functions. Additionally, the prognostic analysis suggested 27 lncRNAs, including LOXL1-AS1, AL356414.1, etc., were significantly associated with patient survival. Given the prognostic significance of these 27 lncRNAs in GBM, we sought to classify the samples. Importantly, Kaplan-Meier analysis revealed that survival times varied significantly among the different categories. Overall, these results identify that the candidate lncRNAs are potential prognostic markers of GBM and its corresponding mRNAs may be a potential target for therapy.

Tumor Mutation Burden-Associated LINC00638/miR-4732-3p/ULBP1 Axis Promotes Immune Escape via PD-L1 in Hepatocellular Carcinoma

Tumor mutation burden (TMB) is associated with immune infiltration, while its underlying mechanism in hepatocellular carcinoma (HCC) remains unclear. A long noncoding RNA (lncRNA)-related competitive endogenous RNA (ceRNA) network can regulate various tumor behaviors, and research about its correlation with TMB and immune infiltration is warranted. Data were downloaded from TCGA and ArrayExpress databases. Cox analysis and machine learning algorithms were employed to establish a lncRNA-based prognostic model for HCC. We then developed a nomogram model to predict overall survival and odds of death for HCC patients. The association of this prognostic model with TMB and immune infiltration was also analyzed. In addition, a ceRNA network was constructed by using DIANA-LncBasev2 and the starBase database and verified by luciferase reporter and colocalization analysis. Multiplex immunofluorescence was applied to determine the correlation between ULBP1 and PD-L1. An eight-lncRNA (SLC25A30-AS1, HPN-AS1, LINC00607, USP2-AS1, HCG20, LINC00638, MKLN1-AS and LINC00652) prognostic score model was constructed for HCC, which was highly associated with TMB and immune infiltration. Next, we constructed a ceRNA network, LINC00638/miR-4732-3p/ULBP1, that may be responsible for NK cell infiltration in HCC with high TMB. However, patients with high ULBP1 possessed a poorer prognosis. Using multiplex immunofluorescence, we found a significant correlation between ULBP1 and PD-L1 in HCC, and patients with high ULBP1 and PD-L1 had the worst prognosis. In brief, the eight-lncRNA model is a reliable tool to predict the prognosis of HCC patients. The LINC00638/miR-4732-3p/ULBP1 axis may regulate immune escape via PD-L1 in HCC with high TMB.