Identification of a putative competitive endogenous RNA network for lung adenocarcinoma using TCGA datasets

Crosstalk among disulfidptosis-related lncRNAs in lung adenocarcinoma reveals a correlation with immune profile and clinical prognosis

Disulfidptosis refers to a specific programmed cell death process characterized by the accumulation of disulfides. It has recently been reported in several cancers. However, the impact of disulfidptosis-related long non-coding RNAs (lncRNAs) on malignant tumors has remained largely unknown. In the present work, we screened prognostic disulfidptosis-related lncRNAs and studied their effects on lung adenocarcinoma. Relevant clinical data of lung adenocarcinoma cases were retrieved from The Cancer Genome Atlas (TCGA) database. RNA sequencing was used to identify differentially expressed disulfidptosis-related lncRNAs within lung adenocarcinoma. In addition, prognostic disulfidptosis-related lncRNAs were obtained through univariate Cox regression analysis. LASSO-COX was used to construct new disulfidptosis-related lncRNA signatures. Different statistical approaches were used to validate the practicability and accuracy of the disulfidptosis-related lncRNAs signatures. Furthermore, several bioinformatic approaches were used to study relevant heterogeneities in biological processes and pathways of diverse risk groups. Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was conducted to analyze the expression of disulfidptosis-related lncRNAs. Finally, seven disulfidptosis-related lncRNA signatures were identified in lung adenocarcinoma cells. The prognosis prediction model constructed efficiently predicted patient survival. Subgroup analysis revealed significant differences in immune cell proportion, including T follicular helper cells and M0 macrophages. In addition, in vitro experimental results demonstrated significant differences in disulfidptosis-related lncRNAs. Altogether, the six disulfidptosis-related lncRNA signatures could serve as a potential prognostic biomarker for lung adenocarcinoma. Furthermore, these can be used as a prediction model in individualized immunotherapy for lung adenocarcinoma.

Identification of cancer types from gene expressions using learning techniques

Tumor is the major cause of death all around the world in recent days. Early detection and prediction of a cancer type are important for a patient's well-being. Functional genomic data has recently been used in the effective and early detection of cancer. According to previous research, the use of microarray data in cancer prediction has evidenced two main problems as high dimensionality and limited sample size. Several researchers have used numerous statistical and machine learning-based methods to classify cancer types but still, limitations are there which makes cancer classification a difficult job. Deep Learning (DL) and Convolutional Neural Networks (CNN) have been proven with effective analyses of unstructured data including gene expression data. In the proposed method gene expression data for five types of cancer is collected from The Cancer Genome Atlas (TCGA). Prominent features are selected using a hybrid Particle Swarm Optimization (PSO) and Random Forest (RF) algorithm followed by the use of Principal Component Analysis (PCA) for dimensionality reduction. Finally, for classification blend of Convolutional Neural Network (CNN) and Bi-directional Long Short Term Memory (Bi-LSTM) is used to predict the target type of cancer. Experimental results demonstrate that accuracy of the proposed method is 96.89%. As compared to existing work, our method outperformed with better results.

Characterization and validation of a ferroptosis-related LncRNA signature as a novel prognostic model for lung adenocarcinoma in tumor microenvironment

Ferroptosis is a more relatively recently identified type of programmed cell death, which is associated with tumor progression. However, the mechanism underlying the effect of ferroptosis-related long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains elusive. Therefore, the current study aimed to investigate the role of ferroptosis-related lncRNAs in LUAD and to develop a prognostic model. The clinicopathological characteristics of patients and the gene sequencing data were obtained from The Cancer Genome Atlas, while the ferroptosis-associated mRNAs were downloaded from the FerrDb database. A ferroptosis-related lncRNA signature was established with Least Absolute Shrinkage and Selection Operator Cox regression analysis. Furthermore, the risk scores of ferroptosis-related lncRNAs were calculated and LUAD patients were then assigned to high- and low-risk groups based on the median risk score. The prognostic model was established by K-M plotters and nomograms. Gene set enrichment analysis (GSEA) was performed to evaluate the association between immune responses and ferroptosis-related lncRNAs. A total of 10 ferroptosis-related lncRNAs were identified as independent predictors of LUAD outcome, namely RP11-386M24.3, LINC00592, FENDRR, AC104699.1, AC091132.1, LANCL1-AS1, LINC-PINT, IFNG-AS1, LINC00968 and AC006129.2. The area under the curve verified that the established signatures could determine LUAD prognosis. The nomogram model was used to assess the predictive accuracy of the established signatures. Additionally, GSEA revealed that the 10 ferroptosis-related lncRNAs could be involved in immune responses in LUAD. Overall, the results of the current study may provide novel insights into the development of novel therapies or diagnostic strategies for LUAD.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Detection of Prognostic Biomarkers for Hepatocellular Carcinoma through CircRNA-associated CeRNA Analysis

BACKGROUND AND AIMS

The prognosis of hepatocellular carcinoma (HCC) is extremely poor; therefore, there is an urgent need for novel prognostic molecular biomarkers of HCC. The current investigation utilized circular (circ)RNA-associated competing endogenous (ce)RNAs analysis in order to identify significant prognostic biomarkers of HCC.

METHODS

CircRNAs and mRNAs that were differentially expressed between normal and HCC tissues were identified. Their respective functions were predicted with Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A nomogram was used for model verification.

RESULTS

A ceRNA network composed of differentially expressed circRNAs and mRNAs was constructed. Significant hub nodes in the ceRNA network were hsa_circ_0004662, hsa_circ_0005735, hsa_circ_0006990, hsa_circ_0018403 and hsa_circ_0100609. By using this information, a prognostic risk assessment tool was developed based on the expressions of seven genes (PLOD2, TARS, RNF19B, CCT2, RAN, C5orf30 and MCM10). Furthermore, multivariate Cox regression analysis revealed risk and T-stage parameters as independent prognostic factors. The nomograms that were constructed from risk and T-stage groups were used to further assess the prediction of HCC patient survival rates. The nomogram, which consisted of risk and T-stage scores assessment models, was found to be an independent factor for predicting prognosis of HCC.

CONCLUSIONS

Five circRNAs, including hsa_circ_0004662, hsa_circ_0005735, hsa_circ_0006990, hsa_circ_0018403 and hsa_circ_0100609, that may play key roles in the progression of HCC were identified. Seven gene signatures were identified, which were associated with the aforementioned circRNAs, including PLOD2, TARS, RNF19B, CCT2, RAN, C5orf30 and MCM10, all of which were significant genes involved in the pathophysiology of HCC. These genes may be used as a prognosticating tool in HCC patients.

GAS6-AS1 Overexpression Increases GIMAP6 Expression and Inhibits Lung Adenocarcinoma Progression by Sponging miR-24-3p

GAS6 antisense RNA 1 (GAS6-AS1) is a long non-coding RNA involved in hepatocellular carcinoma and gastric cancer. However, the functional role of GAS6-AS1 in lung adenocarcinoma (LUAD) remains unclear. In the present study, qRT-PCR was used to measure the levels of GAS6-AS1, GIMAP6 and miR-24-3p expression in LUAD samples and cell lines. CCK-8 and colony formation assays were used to determine cell proliferation. Cell migration and invasion were evaluated using wound healing and transwell assays, respectively. The potential interactions between molecules were assessed using RNA immunoprecipitation and luciferase reporter assays. Western blot analysis was used to quantify protein expression. The anti-tumor effect of over-expressed GAS6-AS1 on LUAD was also examined in vivo in xenograft tumor experiments. The expression of GAS6-AS1 was notably downregulated in LUAD samples and cell lines and associated with a poor prognosis. GAS6-AS1 overexpression inhibited the migration and invasion of A549 and H1650 cells. Down-expressed GAS6-AS1 acted as a sponge for miR-24-3p and down-regulated the expression of its target, GTPase IMAP Family Member 6. These findings suggested that GAS6-AS1 might represent a potential diagnostic biomarker for LUAD.

Long noncoding RNA POU6F2-AS1 regulates lung cancer aggressiveness through sponging miR-34c-5p to modulate KCNJ4 expression

It has been extensively reported that long noncoding RNAs (lncRNAs) were closely associated with multiple malignancies. The aim of our study was to investigate the effects and mechanism of lncRNA POU6F2-AS1 in lung adenocarcinoma (LADC).The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets provided us the information of LADC clinical samples. High-regulation of POU6F2-AS1 was presented in LADC tissues compared with adjacent normal tissues, which was correlated with poor outcome of LADC patients. Functional experiments in Calu-3 and NCI-H460 cells showed that POU6F2-AS1 significantly promoted LADC cell proliferation, colony formation, invasion and migration. Moreover, through online prediction, luciferase reporter assay and Pearson's correlation analysis, we found that POU6F2-AS1 may act as a competing endogenous RNA (ceRNA) of miR-34c-5p and facilitated the expression of potassium voltage-gated channel subfamily J member 4 (KCNJ4). The promoting effect of cell aggressiveness induced by POU6F2-AS1 was enhanced by KCNJ4, whilst was abrogated due to the overexpression of miR-34c-5p. Collectively, POU6F2-AS1 might function as a ceRNA through sponging miR-34c-5p to high-regulate KCNJ4 in LADC, which indicates that POU6F2-AS1 might be a promising therapeutic target with significant prognostic value for LADC treatment.

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

Background

Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients.

Methods

Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model.

Results

There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients.

Conclusion

The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

AURKA and FAM83A are prognostic biomarkers and correlated with Tumor-infiltrating Lymphocytes in smoking related Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) has become the main histologic type, which account for nearly 40% of lung cancer. The present study aimed to investigate the gene expression signature in smoking related LUAD. A total of 45 smoking related DEGs in LUAD were identified and functional enrichment analysis was also performed. Then Cox's regression model and Kaplan-Meier analysis were used to screen potential prognostic genes. Finally, AURKA and FAM83A were left for further immune-related mechanism exploration. Kaplan-Meier analysis indicated survival rates are related to different immune cell (B cell and Dendritic cell) infiltration levels. Mechanistically, we further explore the correlation between AURKA and FAM83A gene expression levels and tumor-infiltrating lymphocytes (TILs) level as well as their response to immunomodulators. The results suggested that AURKA and FAM83A are highly expressed in smoking related LUAD, and negatively correlated to B cell and Dendritic cell infiltration levels. At the same time, B cell and Dendritic cell infiltration levels also related to the prognosis of LUAD. We further revealed AURKA and FAM83A could be novel targets to improve the prognosis of LUAD through regulated the response to immunomodulators.

Identification of LINC02310 as an enhancer in lung adenocarcinoma and investigation of its regulatory network via comprehensive analyses

BACKGROUND

Lung adenocarcinoma (LADC) is a major subtype of non-small cell lung cancer and has one of the highest mortality rates. An increasing number of long non-coding RNAs (LncRNAs) were reported to be associated with the occurrence and progression of LADC. Thus, it is necessary and reasonable to find new prognostic biomarkers for LADC among LncRNAs.

METHODS

Differential expression analysis, survival analysis, PCR experiments and clinical feature analysis were performed to screen out the LncRNA which was significantly related to LADC. Its role in LADC was verified by CCK-8 assay and colony. Furthermore, competing endogenous RNA (ceRNA) regulatory network construction, enrichment analysis and protein-protein interaction (PPI) network construction were performed to investigate the downstream regulatory network of the selected LncRNA.

RESULTS

A total of 2431 differentially expressed LncRNAs (DELncRNAs) and 2227 differentially expressed mRNAs (DEmRNAs) were from The Cancer Genome Atlas database. Survival analysis results indicated that lnc-YARS2-5, lnc-NPR3-2 and LINC02310 were significantly related to overall survival. Their overexpression indicated poor prognostic. PCR experiments and clinical feature analysis suggested that LINC02310 was significantly correlated with TNM-stage and T-stage. CCK-8 assay and colony formation assay demonstrated that LINC02310 acted as an enhancer in LADC. In addition, 3 targeted miRNAs of LINC02310 and 414 downstream DEmRNAs were predicted. The downstream DEmRNAs were then enriched in 405 Gene Ontology terms and 11 Kyoto Encyclopedia of Genes and Genomes pathways, which revealed their potential functions and mechanisms. The PPI network showed the interactions among the downstream DEmRNAs.

CONCLUSIONS

This study verified LINC02310 as an enhancer in LADC and performed comprehensive analyses on its downstream regulatory network, which might benefit LADC prognoses and therapies.

Emerging impact of the long noncoding RNA MIR22HG on proliferation and apoptosis in multiple human cancers

An increasing number of studies have shown that long noncoding RNAs (lncRNAs) play important roles in diverse cellular processes, including proliferation, apoptosis, migration, invasion, chromatin remodeling, metabolism and immune escape. Clinically, the expression of MIR22HG is increased in many human tumors (colorectal cancer, gastric cancer, hepatocellular carcinoma, lung cancer, and thyroid carcinoma), while in others (esophageal adenocarcinoma and glioblastoma), it is significantly decreased. Moreover, MIR22HG has been reported to function as a competitive endogenous RNA (ceRNA), be involved in signaling pathways, interact with proteins and interplay with miRNAs as a host gene to participate in tumorigenesis and tumor progression. In this review, we describe the biological functions of MIR22HG, reveal its underlying mechanisms for cancer regulation, and highlight the potential role of MIR22HG as a novel cancer prognostic biomarker and therapeutic target that can increase the efficacy of immunotherapy and targeted therapy for cancer treatment.

Characterization of a non-coding RNA-associated ceRNA network in metastatic lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a highly malignant cancer. Although competing endogenous RNA (ceRNA)-based profiling has been investigated in patients with LUAD, it has not been specifically used to study metastasis in LUAD. We found 130 differentially expressed (DE) lncRNAs, 32 DE miRNAs and 981 DE mRNAs from patients with LUAD in The Cancer Genome Atlas (TCGA) database. We analysed the functions and pathways of 981 DE mRNAs using the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Based on the target DE mRNAs and DE lncRNAs of DE miRNAs, we established an lncRNA-miRNA-mRNA ceRNA network, comprising 37 DE lncRNAs, 22 DE miRNAs and 212 DE mRNAs. Subsequently, we constructed a protein-protein interaction network of DE mRNAs in the ceRNA network. Among all, DE RNAs, 5 DE lncRNAs, 5 DE miRNAs and 45 DE mRNAs were confirmed found to be associated with clinical prognosis. Moreover, 3 DE lncRNAs, 4 DE miRNAs and 9 DE mRNAs in the ceRNA network were associated with clinical prognosis. We further screened 3 DE lncRNAs, 3 DE miRNAs and 3 DE mRNAs using clinical samples. These DE lncRNAs, DE miRNAs and DE mRNAs in ceRNA network may serve as independent biomarkers of LUAD metastasis.

Bioinformatics Analysis to Reveal Potential Differentially Expressed Long Non-Coding RNAs and Genes Associated with Tumour Metastasis in Lung Adenocarcinoma

Background

Due to the onset of metastases, the survival rate of lung adenocarcinoma (LUAD) is still low. In view of this, we performed this study to screen metastasis-associated genes and lncRNAs in LUAD.

Methods

The mRNA and lncRNA expression profiles of 185 metastatic LUAD and 217 non-metastatic LAUD samples were retrieved from the TCGA database and included in this study. The differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) between metastatic samples and non-metastatic samples of LAUD, as well as the cis nearby-targeted DEmRNAs of DElncRNAs and the DElncRNA-DEmRNA co-expression network, were obtained. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of selected DEmRNAs. Survival analysis of selected DElncRNAs and DEmRNAs was performed.

Results

In total, 1351 DEmRNAs and 627 DElncRNAs were screened between the LUAD primary tissue samples and metastatic samples. Then, 194 DElncRNA-nearby-targeted DEmRNA pairs and 191 DElncRNA-DEmRNA co-expression pairs were detected. Except for RHCG and KRT81, the expression of the other six DEmRNAs in the qRT-PCR results generally exhibited the same pattern as that in our integrated analysis. The expression of CRHR2, FAM83A-AS1, FAM83A and Z83843.1 was significantly correlated with the overall survival time of patients with metastatic LUAD.

Conclusion

We speculate that two interaction pairs (FAM83A-AS1-FAM83A and Z83843.1-MATR3) and four genes (CRHR2, UGT2B15, CHGB and NEFL) are closely associated with the metastasis of LUAD.