Five-long non-coding RNA risk score system for the effective prediction of gastric cancer patient survival

Longitudinal multiomics analysis of aggressive pituitary neuroendocrine tumors: comparing primary and recurrent tumors from the same patient, reveals genomic stability and heterogeneous transcriptomic profiles with alterations in metabolic pathways

Pituitary neuroendocrine tumors (PitNET) represent the vast majority of sellar masses. Some behave aggressively, growing rapidly and invading surrounding tissues, with high rates of recurrence and resistance to therapy. Our aim was to establish patterns of genomic, transcriptomic and methylomic evolution throughout time in primary and recurrent tumors from the same patient. Therefore, we performed transcriptome- and exome-sequencing and methylome microarrays of aggressive, primary, and recurrent PitNET from the same patient. Primary and recurrent tumors showed a similar exome profile, potentially indicating a stable genome over time. In contrast, the transcriptome of primary and recurrent PitNET was dissimilar. Gonadotroph, silent corticotroph, as well as metastatic corticotroph and a somatotroph PitNET expressed genes related to fatty acid biosynthesis and metabolism, phosphatidylinositol signaling, glycerophospholipid and phospholipase D signaling, respectively. Diacylglycerol kinase gamma (DGKG), a key enzyme in glycerophospholipid metabolism and phosphatidylinositol signaling pathways, was differentially expressed between primary and recurrent PitNET. These alterations did not seem to be regulated by DNA methylation, but rather by several transcription factors. Molecular docking showed that dasatinib, a small molecule tyrosine kinase inhibitor used in the treatment of chronic lymphocytic and acute lymphoblastic leukemia, could target DGKG. Dasatinib induced apoptosis and decreased proliferation in GH3 cells. Our data indicate that pituitary tumorigenesis could be driven by transcriptomically heterogeneous clones, and we describe alternative pharmacological therapies for aggressive and recurrent PitNET.

GAS6-AS1, a long noncoding RNA, functions as a key candidate gene in atrial fibrillation related stroke determined by ceRNA network analysis and WGCNA

BACKGROUND

Stroke attributable to atrial fibrillation (AF related stroke, AFST) accounts for 13 ~ 26% of ischemic stroke. It has been found that AFST patients have a higher risk of disability and mortality than those without AF. Additionally, it's still a great challenge to treat AFST patients because its exact mechanism at the molecular level remains unclear. Thus, it's vital to investigate the mechanism of AFST and search for molecular targets of treatment. Long non-coding RNAs (lncRNAs) are related to the pathogenesis of various diseases. However, the role of lncRNAs in AFST remains unclear. In this study, AFST-related lncRNAs are explored using competing endogenous RNA (ceRNA) network analysis and weighted gene co-expression network analysis (WGCNA).

METHODS

GSE66724 and GSE58294 datasets were downloaded from GEO database. After data preprocessing and probe reannotation, differentially expressed lncRNAs (DELs) and differentially expressed mRNAs (DEMs) between AFST and AF samples were explored. Then, functional enrichment analysis and protein-protein interaction (PPI) network analysis of the DEMs were performed. At the meantime, ceRNA network analysis and WGCNA were performed to identify hub lncRNAs. The hub lncRNAs identified both by ceRNA network analysis and WGCNA were further validated by Comparative Toxicogenomics Database (CTD).

RESULTS

In all, 19 DELs and 317 DEMs were identified between the AFST and AF samples. Functional enrichment analysis suggested that the DEMs associated with AFST were mainly enriched in the activation of the immune response. Two lncRNAs which overlapped between the three lncRNAs identified by the ceRNA network analysis and the 28 lncRNAs identified by the WGCNA were screened as hub lncRNAs for further validation. Finally, lncRNA GAS6-AS1 turned out to be associated with AFST by CTD validation.

CONCLUSION

These findings suggested that low expression of GAS6-AS1 might exert an essential role in AFST through downregulating its downstream target mRNAs GOLGA8A and BACH2, and GAS6-AS1 might be a potential target for AFST therapy.

Downregulation of Linc00173 increases BCL2 mRNA stability via the miR-1275/PROCA1/ZFP36L2 axis and induces acquired cisplatin resistance of lung adenocarcinoma

BACKGROUND

LINC00173 had been reported as a cisplatin (cis-diamminedichloroplatinum, DDP) chemotherapy-resistant inducer in small-cell lung cancer (SCLC) and lung squamous cell carcinoma (LUSC). This study aimed to display reverse data for LINC00173 as a DDP chemosensitivity-inducing factor in lung adenocarcinoma (LUAD).

METHODS

LINC00173 was screened from the Gene Expression Omnibus database (GSE43493). The expression level of LINC00173 in LUAD tissues and cell lines was detected using in situ hybridization and quantitative reverse transcription-polymerase chain reaction. Colony formation, cell viability, half-maximal inhibitory concentration, flow cytometry, and xenograft mouse model were used to evaluate the role of LINC00173 in the chemosensitivity of LUAD to DDP. The mechanism of LINC00173 in DDP resistance by mediating miR-1275/PROCA1/ZFP36L2 axis to impair BCL2 mRNA stability was applied, and co-immunoprecipitation, chromatin immunoprecipitation, RNA antisense purification, RNA immunoprecipitation, and luciferase reporter assays were performed.

RESULTS

LINC00173 downregulation in patients with DDP-resistant LUAD was correlated with poor prognosis. Further, LINC00173 expression was significantly reduced in DDP-resistant LUAD cells and DDP-treated human LUAD tissues. Suppressed LINC00173 expression in LUAD cells enhanced DDP chemoresistance in vivo and in vitro, while restored LINC00173 expression in DDP-resistant LUAD cells markedly regained chemosensitivity to DDP. Mechanistically, DDP-resistant LUAD cells activated PI3K/AKT signal and further elevated the c-Myc expression. The c-Myc, as an oncogenic transcriptional factor, bound to the promoter of LINC00173 and suppressed its expression. The reduced LINC00173 expression attenuated the adsorption of oncogenic miR-1275, downregulating the expression of miR-1275 target gene PROCA1. PROCA1 played a potential tumor-suppressive role inducing cell apoptosis and DDP chemosensitivity via recruiting ZFP36L2 to bind to the 3' untranslated region of BCL2, reducing the stability of BCL2 mRNA and thus activating the apoptotic signal.

CONCLUSIONS

This study demonstrated a novel and critical role of LINC00173. It was transcriptionally repressed by DDP-activated PI3K/AKT/c-Myc signal in LUAD, promoting DDP-acquired chemotherapeutic resistance by regulating miR-1275 to suppress PROCA1/ZFP36L2-induced BCL2 degradation, which led to apoptotic signal reduction. These data were not consistent with the previously described role of LINC00173 in SCLC or LUSC, which suggested that LINC00173 could play fine-tuned DDP resistance roles in different pathological subtypes of lung cancer. This study demonstrated that the diminished expression of LINC00173 might serve as an indicator of DDP-acquired resistance in LUAD.

Long Intergenic Non-Protein Coding RNA 173 in Human Cancers

Long non-coding RNAs belong to non-coding RNAs (ncRNAs) with a length of more than 200 nucleotides and limited protein-coding ability. Growing research has clarified that dysregulated lncRNAs are correlated with the development of various complex diseases, including cancer. LINC00173 has drawn researchers' attention as one of the recently discovered lncRNAs. Aberrant expression of LINC00173 affects the initiation and progression of human cancers. In the present review, we summarize the recent considerable research on LINC00173 in 11 human cancers. Through the summary of the abnormal expression of LINC00173 and its potential molecular regulation mechanism in cancers, this article indicates that LINC00173 may serve as a potential diagnostic biomarker and a target for drug therapy, thus providing novel clues for future related research.

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

Objective. N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods. In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results. Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.

Regulatory Roles of Noncoding RNAs in the Progression of Gastrointestinal Cancers and Health Disparities

Annually, more than a million individuals are diagnosed with gastrointestinal (GI) cancers worldwide. With the advancements in radio- and chemotherapy and surgery, the survival rates for GI cancer patients have improved in recent years. However, the prognosis for advanced-stage GI cancers remains poor. Site-specific GI cancers share a few common risk factors; however, they are largely distinct in their etiologies and descriptive epidemiologic profiles. A large number of mutations or copy number changes associated with carcinogenesis are commonly found in noncoding DNA regions, which transcribe several noncoding RNAs (ncRNAs) that are implicated to regulate cancer initiation, metastasis, and drug resistance. In this review, we summarize the regulatory functions of ncRNAs in GI cancer development, progression, chemoresistance, and health disparities. We also highlight the potential roles of ncRNAs as therapeutic targets and biomarkers, mainly focusing on their ethnicity-/race-specific prognostic value, and discuss the prospects of genome-wide association studies (GWAS) to investigate the contribution of ncRNAs in GI tumorigenesis.

FAM201A, a long noncoding RNA potentially associated with atrial fibrillation identified by ceRNA network analyses and WGCNA

Background

Being the most common arrhythmia in clinic, atrial fibrillation (AF) causes various comorbidities to patients such as heart failure and stroke. LncRNAs were reported involved in pathogenesis of AF, yet, little is known about AF-associated lncRNAs. The present study aims to explore lncRNAs associated with AF susceptibility based on competing endogenous RNA (ceRNA) network analysis and weighted gene co-expression network analysis (WGCNA).

Methods

GSE41177 and GSE79768 datasets were obtained from the Gene Expression Omnibus (GEO) database. Competing endogenous RNA (ceRNA) network analysis was performed using GSE41177. Differentially expressed lncRNAs (DElncRNAs), mRNAs (DEmRNAs) between AF patients and patients with sinus rhythm (SR) were identified from GSE41177 using R software. Then, the ceRNA network was constructed based on DElncRNAs, the predicted target miRNAs and DEmRNAs. Weighted gene co-expression network analysis (WGCNA) was performed using GSE79768 to validate the AF-related lncRNAs identified from GSE41177. LncRNA modules and crucial lncRNAs relevant to AF and were identified.

Results

In summary, 18 DElncRNAs and 350 DEmRNAs were found between AF patients and SR patients. A total of 5 lncRNAs, 10 miRNAs, and 21 mRNAs were contained in the final ceRNA network. Taking into consideration both the ceRNA theory and inference scores from the comparative toxicogenomics database (CTD) database, the ceRNA axis FAM201A-miR-33a-3p-RAC3 was identified as mostly relevant to AF susceptibility. FAM201A (Gene significance, GS = − 0.62; Module membership, MM = 0.75) was also proved in the blue module, which was identified most highly relevant with AF by WGCNA.

Conclusions

These results demonstrated that decreased expression of FAM201A might be associated with susceptibility of AF. Working as the ceRNA to regulate RAC3 might be one function of FAM201A in AF susceptibility, which requires further exploration in future research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01232-w.

Weighted correlation network analysis revealed novel long non-coding RNAs for colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, which after breast, lung and, prostate cancers, is the fourth prevalent cancer in the United States. Long non-coding RNAs (lncRNAs) have an essential role in the pathogenesis of CRC. Therefore, bioinformatics studies on lncRNAs and their target genes have potential importance as novel biomarkers. In the current study, publicly available microarray gene expression data of colorectal cancer (GSE106582) was analyzed with the Limma, Geoquery, Biobase package. Afterward, identified differentially expressed lncRNAs and their target genes were inserted into Weighted correlation network analysis (WGCNA) to obtain modules and hub genes. A total of nine differentially expressed lncRNAs (LINC01018, ITCH-IT, ITPK1-AS1, FOXP1-IT1, FAM238B, PAXIP1-AS1, ATP2B1-AS1, MIR29B2CHG, and SNHG32) were identified using microarray data analysis. The WGCNA has identified several hub genes for black (LMOD3, CDKN2AIPNL, EXO5, ZNF69, BMS1P5, METTL21A, IL17RD, MIGA1, CEP19, FKBP14), blue (CLCA1, GUCA2A, UGT2B17, DSC2, CA1, AQP8, ITLN1, BEST4, KLF4, IQCF6) and turquoise (PAFAH1B1, LMNB1, CACYBP, GLO1, PUM3, POC1A, ASF1B, SDCCAG3, ASNS, PDCD2L) modules. The findings of the current study will help to improve our understanding of CRC. Moreover, the hub genes that we have identified could be considered as possible prognostic/diagnostic biomarkers. This study led to the determination of nine lncRNAs with no previous association with CRC development.

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.

Methods

CIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan-Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.

Results

Red module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.

Conclusion

We developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

Identification of Potential Signatures and Their Functions for Acute Lymphoblastic Leukemia: A Study Based on the Cancer Genome Atlas

Objective

Acute lymphoblastic leukemia (ALL) is a malignant disease most commonly diagnosed in adolescents and young adults. This study aimed to explore potential signatures and their functions for ALL.

Methods

Differentially expressed mRNAs (DEmRNAs) and differentially expressed long non-coding RNAs (DElncRNAs) were identified for ALL from The Cancer Genome Atlas (TCGA) and normal control from Genotype-Tissue Expression (GTEx). DElncRNA-microRNA (miRNA) and miRNA-DEmRNA pairs were predicted using online databases. Then, a competing endogenous RNA (ceRNA) network was constructed. Functional enrichment analysis of DEmRNAs in the ceRNA network was performed. Protein-protein interaction (PPI) network was then constructed. Hub genes were identified. DElncRNAs in the ceRNA network were validated using Real-time qPCR.

Results

A total of 2,903 up- and 3,228 downregulated mRNAs and 469 up- and 286 downregulated lncRNAs were identified for ALL. A ceRNA network was constructed for ALL, consisting of 845 lncRNA-miRNA and 395 miRNA-mRNA pairs. These DEmRNAs in the ceRNA network were mainly enriched in ALL-related biological processes and pathways. Ten hub genes were identified, including SMAD3, SMAD7, SMAD5, ZFYVE9, FKBP1A, FZD6, FZD7, LRP6, WNT1, and SFRP1. According to Real-time qPCR, eight lncRNAs including ATP11A-AS1, ITPK1-AS1, ANO1-AS2, CRNDE, MALAT1, CACNA1C-IT3, PWRN1, and WT1-AS were significantly upregulated in ALL bone marrow samples compared to normal samples.

Conclusion

Our results showed the lncRNA expression profiles and constructed ceRNA network in ALL. Furthermore, eight lncRNAs including ATP11A-AS1, ITPK1-AS1, ANO1-AS2, CRNDE, MALAT1, CACNA1C-IT3, PWRN1, and WT1-AS were identified. These results could provide a novel insight into the study of ALL.

Hsa_circ_0026134 expression promoted TRIM25- and IGF2BP3-mediated hepatocellular carcinoma cell proliferation and invasion via sponging miR-127-5p

Increasing evidence shows that circular RNAs (circRNAs) play a regulatory role in cancer. In the present study, we aimed to investigate the characteristics and effects of hsa_circ_0026134 in hepatocellular carcinoma (HCC). We investigated hsa_circ_0026134 expression in 20 pairs of clinical tissues from HCC patients; expression of hsa_circ_0026134 in different cell lines; effect of hsa_circ_0026134 on proliferation and invasion of HCC cell lines; and the regulatory mechanisms and interactions among hsa_circ_0026134, miR-127-5p, tripartite motif-containing protein 25 (TRIM25) and insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3). hsa_circ_0026134 expression was increased in HCC samples and cell lines. Down-regulation of hsa_circ_0026134 attenuated HCC cell proliferation and metastatic properties. Micro (mi)RNA (miR)-127-5p was sponged by hsa_circ_0026134. Rescue experiments indicated that inhibition of miR-127-5p expression promoted cell proliferation and invasion even after hsa_circ_0026134 silencing. TRIM25 and IGF2BP3 were targets of miR-127-5p. Overexpression of TRIM25 or IGF2BP3 promoted cell proliferation and invasion in cells overexpressing miR-127-5p. Down-regulation of hsa_circ_0026134 suppressed TRIM25- and IGF2BP3-mediated HCC cell proliferation and invasion via promotion of miR-127-5p expression, which have been confirmed by luciferase reporter assay. The present study provides a new treatment target for HCC.

RNA Sequencing Reveals LINC00167 as a Potential Diagnosis Biomarker for Primary Osteoarthritis: A Multi-Stage Study

Objectives

Given the roles played by lncRNA in human diseases and the high incidence of OA, this study investigated the pivotal pathways involved in the disease and identified potential biomarkers for OA diagnosis.

Methods

We first performed an exploration of RNA-sequencing in peripheral blood leukocytes from six subjects (3 OA and 3 healthy controls). Promising candidate lncRNAs were evaluated in first stage validation using a GEO dataset (GSE114007) of 38 subjects (20 OA and 18 healthy controls), followed by a second stage validation using quantitative PCR analysis with 101 subjects (67 OA and 34 controls). The third stage investigated the potential value of validated lncRNA in the early diagnosis of OA in peripheral blood leukocytes from a total of 120 participants (60 cases and 60 controls).

Results

The dataset identified a total of 1,380 up-regulated and 719 down-regulated mRNAs and 5,743 up-regulated and 7,384 down-regulated lncRNAs. The up-regulated DEGs were mainly enriched in the extracellular matrix, while the down-regulated DEGs were mainly enriched in the IL-17 and wnt signaling pathways. 18 overlapping candidate lncRNAs survived after first-stage validation. 3 hub lncRNAs were selected for the second validation stage and qualified in an external sample, and lncRNA LINC00167 was further confirmed with a similar result (down-expressed in both stages). Receiver operating characteristic analysis showed that LINC00167 can distinguish OA cases from healthy controls with a high area under the curve of 0.879 (95%CI: 0.819, 0.938; P < 0.001), with a sensitivity of 80.7% and specificity of 83.5%.

Conclusion

The expression profile of OA was identified and critical pathways were elucidated by an integrated approach to RNA-seq from easily accessible blood. LINC00167 may serve as a potential early diagnosis marker for OA in clinical practice. The detailed mechanism of action of this lncRNA requires further elucidation in future studies.

LINC00173 promotes the apoptosis of hypertrophic scar fibroblasts through increasing β-catenin expression

Previous studies have demonstrated the involvement of long intergenic nonprotein coding RNA 173 (LINC00173) in several pathological disorders. However, the function of LINC00173 in the hypertrophic scar is not well understood. This study confirmed that the two transcript variants of TSV1 and TSV2 were both upregulated in hypertrophic scar fibroblasts. The overexpression of TSV1 or TSV2 promoted the apoptosis of fibroblasts, whereas the overexpression of TSV2 inhibited the proliferation of fibroblasts. RNA-sequencing (RNA-seq), Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA) showed that phosphatidylinositol 3-kinase (PI3K)/Akt and Mitogen-activated protein kinases (MAPK) signaling might be involved in the role of LINC00173 in hypertrophic scar pathogenesis. Furthermore, the protein expression of β-catenin was upregulated in the TSV1 or TSV2 overexpression group. Overall, the study demonstrated that LINC00173 promoted the apoptosis of fibroblasts through increasing β-catenin expression, suggesting that LINC00173 might be a new target for hypertrophic scar treatment.

Epigenome-Wide Association Study Using Prediagnostic Bloods Identifies New Genomic Regions Associated With Pancreatic Cancer Risk

BACKGROUND

Epigenome-wide association studies using peripheral blood have identified specific sites of DNA methylation associated with risk of various cancers and may hold promise to identify novel biomarkers of risk; however, few studies have been performed for pancreatic cancer and none using a prospective study design.

METHODS

Using a nested case-control study design, incident pancreatic cancer cases and matched controls were identified from participants who provided blood at baseline in 3 prospective cohort studies. DNA methylation levels were measured in DNA extracted from leukocytes using the Illumina MethylationEPIC array. Average follow-up period for this analysis was 13 years.

RESULTS

Several new genomic regions were identified as being differentially methylated in cases and controls; the 5 strongest associations were observed for CpGs located in genes TMEM204/IFT140, MFSD6L, FAM134B/RETREG1, KCNQ1D, and C6orf227. For some CpGs located in chromosome 16p13.3 (near genes TMEM204 and IFT140), associations were stronger with shorter time to diagnosis (eg, odds ratio [OR] = 5.95, 95% confidence interval [CI] = 1.52 to 23.12, for top vs bottom quartile, for <5 years between blood draw and cancer diagnosis), but associations remained statistically significantly higher even when cases were diagnosed over 10 years after blood collection. Statistically significant differences in DNA methylation levels were also observed in the gastric secretion pathway using Gene Set Enrichment Analysis (GSEA) analysis.

CONCLUSIONS

Changes in DNA methylation in peripheral blood may mark alterations in metabolic or immune pathways that play a role in pancreatic cancer. Identifying new biological pathways in carcinogenesis of pancreatic cancer using epigenome-wide association studies approach could provide new opportunities for improving treatment and prevention.

Competing endogenous RNA network analysis for screening inflammation‑related long non‑coding RNAs for acute ischemic stroke

Long non-coding RNAs (lncRNAs) represent potential biomarkers for the diagnosis and treatment of various diseases; however, the role of circulating acute ischemic stroke (AIS)-related lncRNAs remains relatively unknown. The present study aimed to screen crucial lncRNAs for AIS based on the competing endogenous RNA (ceRNA) hypothesis. The expression profile datasets for one mRNA, accession no. GSE16561, and four microRNAs (miRNAs), accession nos. GSE95204, GSE86291, GSE55937 and GSE110993, were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs), lncRNAs (DELs), and miRNAs (DEMs) were identified, and ClusterProfiler was used to interpret the function of the DEGs. Based on the protein-protein interaction (PPI) network and module analyses, hub DEGs were identified. A ceRNA network was established based on miRNA-mRNA or miRNA-lncRNA interaction pairs. In total, 2,041 DEGs and 5 DELs were identified between the AIS and controls samples in GSE16561, and 10 DEMs between at least two of the four miRNA expression profiles. A PPI network was constructed with 1,235 DEGs, among which 20 genes were suggested to be hub genes. The hub genes paxillin (PXN), FYN-proto-oncogene, Src family tyrosine kinase (FYN), ras homolog family member A (RHOA), STAT1, and growth factor receptor-bound protein 2 (GRB2), were amongst the most significantly enriched modules extracted from the PPI network. Functional analysis revealed that these hub genes were associated with inflammation-related signaling pathways. An AIS-related ceRNA network was constructed, in which 4 DELs were predicted to function as ceRNAs for 9 DEMs, to regulate the five identified hub genes; that is, minichromosome maintenance complex component 3 associated protein-antisense RNA 1 (MCM3AP-AS1)/long intergenic non-protein coding RNA 1089 (LINC01089)/hsa-miRNA (miR)-125a/FYN, inositol-tetrakisphosphate 1-kinase-antisense RNA 1 (ITPK1-AS1)/hsa-let-7i/RHOA/GRB2/STAT1, and human leukocyte antigen complex group 27 (HCG27)/hsa-miR-19a/PXN interaction axes. In conclusion, MCM3AP-AS1, LINC01089, ITPK1-AS1, and HCG27 may represent new biomarkers and underlying targets for the treatment of AIS.

Construction and Analysis of the Tumor-Specific mRNA-miRNA-lncRNA Network in Gastric Cancer

Weighted correlation network analysis (WGCNA) is a statistical method that has been widely used in recent years to explore gene co-expression modules. Competing endogenous RNA (ceRNA) is commonly involved in the cancer gene expression regulation mechanism. Some ceRNA networks are recognized in gastric cancer; however, the prognosis-associated ceRNA network has not been fully identified using WGCNA. We performed WGCNA using datasets from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) to identify cancer-associated modules. The criteria of differentially expressed RNAs between normal stomach samples and gastric cancer samples were set at the false discovery rate (FDR) < 0.01 and |fold change (FC)| > 1.3. The ceRNA relationships obtained from the RNAinter database were examined by both the Pearson correlation test and hypergeometric test to confirm the mRNA–lncRNA regulation. Overlapped genes were recognized at the intersections of genes predicted by ceRNA relationships, differentially expressed genes, and genes in cancer-specific modules. These were then used for univariate and multivariate Cox analyses to construct a risk score model. The ceRNA network was constructed based on the genes in this model. WGCNA-uncovered genes in the green and turquoise modules are those most associated with gastric cancer. Eighty differentially expressed genes were observed to have potential prognostic value, which led to the identification of 12 prognosis-related mRNAs (KIF15, FEN1, ZFP69B, SP6, SPARC, TTF2, MSI2, KYNU, ACLY, KIF21B, SLC12A7, and ZNF823) to construct a risk score model. The risk genes were validated using the GSE62254 and GSE84433 datasets, with 0.82 as the universal cutoff value. 12 genes, 12 lncRNAs, and 35 miRNAs were used to build a ceRNA network with 86 dysregulated lncRNA–mRNA ceRNA pairs. Finally, we developed a 12-gene signature from both prognosis-related and tumor-specific genes, and then constructed a ceRNA network in gastric cancer. Our findings may provide novel insights into the treatment of gastric cancer.

Crucial Gene Identification in Carotid Atherosclerosis Based on Peripheral Blood Mononuclear Cell (PBMC) Data by Weighted (Gene) Correlation Network Analysis (WGCNA)

Background

Many patients are not responsive or tolerant to medical therapies for carotid atherosclerosis. Thus, elucidating the molecular mechanism for the pathogenesis and progression of carotid atherosclerosis and identifying new potential molecular targets for medical therapies that can slow progression of carotid atherosclerosis and prevent ischemic events are quite important.

Material/Methods

We downloaded the expression profiling data of PBMC in Biobank of Karolinska Endarterectomy (BiKE, GSE21545) for GEO. The WGCNA and DEG screening were conducted. The co-expression pattern between patients with ischemic events (the events group) and patients without ischemic events (the no-events group) were compared. Then, we identified hub genes of each module. Finally, the DEG co-expression network was constructed and MCODE was used to identify crucial genes based on this co-expression network.

Results

In the study, 183 DEGs were screened and 8 and 6 modules were assessed in the events group and no-events group, respectively. Compared to the no-events group, genes associated with inflammation and immune response were clustered in the green-yellow module of the events group. The hub gene of the green-yellow module of the events group was KIR2DL5A. We obtained 1 DEG co-expression network, which has 16 nodes and 24 edges, and we detected 5 crucial genes: SIRT1, THRAP3, RBM43, PEX1, and KLHDC2. The upregulated genes (THRAP3 and RBM43) showed potential diagnostic and prognostic value for the occurrence of ischemic events.

Conclusions

We detected 8 modules for the events group and 6 modules for the no-events group. The hub genes for modules and crucial genes of the DEG co-expression network were also identified. These genes might serve as potential targets for medical therapies and biomarkers for diagnosis and prognosis. Further experimental and biological studies are needed to elucidate the role of these crucial genes in the progression of carotid atherosclerosis.

The Construction and Analysis of the Aberrant lncRNA-miRNA-mRNA Network in Adipose Tissue from Type 2 Diabetes Individuals with Obesity

BACKGROUND

The prevalence of obesity and type 2 diabetes mellitus (T2DM) has become the most serious global public health issue. In recent years, there has been increasing attention to the role of long noncoding RNAs (lncRNAs) in the occurrence and development of obesity and T2DM. The aim of this work was to find new lncRNAs as potential predictive biomarkers or therapeutic targets for obesity and T2DM.

METHODS

In this study, we identified significant differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) between adipose tissue of individuals with obesity and T2DM and normal adipose tissue (absolute log₂FC ≥ 1 and FDR < 0.05). Then, the lncRNA-miRNA interactions predicted by miRcode were further screened with a threshold of MIC > 0.2. Simultaneously, the mRNA-miRNA interactions were explored by miRWalk 2.0. Finally, a ceRNA network consisting of lncRNAs, miRNAs, and mRNAs was established by integrating lncRNA-miRNA interactions and mRNA-miRNA interactions.

RESULTS

Upon comparing adipose tissue from individuals with obesity and T2DM and normal adipose tissues, 364 significant DEmRNAs, including 140 upregulated and 224 downregulated mRNAs, were identified in GSE104674; in addition, 231 significant DEmRNAs, including 146 upregulated and 85 downregulated mRNAs, were identified in GSE133099. GO and KEGG analyses have shown that downregulated DEmRNAs in GSE104674 and GSE133099 were associated with obesity- and T2DM-related biological pathways, such as lipid metabolism, AMPK signaling, and insulin resistance. Furthermore, 28 significant DElncRNAs, including 14 upregulated and 14 downregulated lncRNAs, were found. Based on the predicted lncRNA-miRNA and mRNA-miRNA relationships, we constructed a competitive endogenous RNA (ceRNA) network, including five lncRNAs, ten miRNAs, and 15 mRNAs. KEGG-GSEA analysis revealed that four lncRNAs (FLG-AS1, SNAI3-AS1, AC008147.0, and LINC02015) in the ceRNA network were related to the biological pathways of metabolic diseases.

CONCLUSIONS

Through ceRNA network analysis, our study identified four new lncRNAs that may be used as potential biomarkers and therapeutic targets of obesity and T2DM, thus laying a foundation for future clinical studies.

Identification of crucial genes in abdominal aortic aneurysm by WGCNA

Background

Abdominal aortic aneurysm (AAA) is the full thickness dilation of the abdominal aorta. However, few effective medical therapies are available. Thus, elucidating the molecular mechanism of AAA pathogenesis and exploring the potential molecular target of medical therapies for AAA is of vital importance.

Methods

Three expression datasets (GSE7084, GSE47472 and GSE57691) were downloaded from the Gene Expression Omnibus (GEO). These datasets were merged and then normalized using the “sva” R package. Differential expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were conducted. We compared the co-expression patterns between AAA and normal conditions, and hub genes of each functional module were identified. DEGs were mapped to co-expression network under AAA condition and a DEG co-expression network was generated. Crucial genes were identified using molecular complex detection (MCODE) (a plugin in Cytoscape).

Results

In our study, 6 and 10 gene modules were detected for the AAA and normal conditions, respectively, while 143 DEGs were screened. Compared to the normal condition, genes associated with immune response, inflammation and muscle contraction were clustered in three gene modules respectively under the AAA condition; the hub genes of the three modules were MAP4K1, NFIB and HPK1, respectively. A DEG co-expression network with 102 nodes and 303 edges was identified, and a hub gene cluster with 10 genes from the DEG co-expression network was detected. YIPF6, RABGAP1, ANKRD6, GPD1L, PGRMC2, HIGD1A, GMDS, MGP, SLC25A4 and FAM129A were in the cluster. The expression levels of these 10 genes showed potential diagnostic value.

Conclusion

Based on WGCNA, we detected 6 modules under the AAA condition and 10 modules in the normal condition. Hub genes of each module and hub gene clusters of the DEG co-expression network were identified. These genes may act as potential targets for medical therapy and diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these genes in the pathogenesis of AAA.