WGCNA Co-Expression Network Analysis Reveals ILF3-AS1 Functions as a CeRNA to Regulate PTBP1 Expression by Sponging miR-29a in Gastric Cancer

Identification of the communal pathogenesis and immune landscape between viral myocarditis and dilated cardiomyopathy

AIMS

Studies have confirmed that viral myocarditis (VMC) is one of the risk factors for dilated cardiomyopathy (DCM). The molecular mechanisms underlying the progression from VMC to DCM remain unclear and require further investigation.

METHODS AND RESULTS

The mRNA microarray datasets GSE57338 (DCM) and GSE1145 (VMC) were obtained from the Gene Expression Omnibus database. The candidate key genes were further screened using weighted correlation network analysis (WGCNA), protein-protein interaction and external dataset validation, and the correlation between the candidate key genes and immune cells and the signalling pathways of the candidate key genes were observed by enrichment analysis and immune infiltration analysis. The expression of key genes was validated in the external dataset GSE35182. The crosstalk genes between DCM and VMC were mainly enriched in 'transcriptional misregulation in cancer', 'FoxO signalling pathway', 'AGE-RAGE signalling pathway in diabetic complications', 'thyroid hormone signalling pathway', 'AMPK signalling pathway', and other signalling pathways. The immune infiltration analysis indicated that VMC was mainly associated with resting dendritic cells and M0 macrophages, while DCM was mainly associated with monocytes, M0 macrophages, CD8+ T cells, resting CD4 memory T cells, naive CD4+ T cells, and resting mast cells. In DCM-related dataset GSE57338 and VMC-related dataset GSE1145, a total of 18 candidate key genes were differentially expressed. BLC6, FOXO1, and UBE2M were identified as the key genes that lead to the progression from VMC to DCM by GSE35182.

CONCLUSIONS

Three key genes (BLC6, FOXO1, and UBE2M) were identified and provided new insights into the diagnosis and treatment of VMC with DCM.

Application of Machine Learning in Predicting Hepatic Metastasis or Primary Site in Gastroenteropancreatic Neuroendocrine Tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) account for 80% of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). GEP-NETs are well-differentiated tumors, highly heterogeneous in biology and origin, and are often diagnosed at the metastatic stage. Diagnosis is commonly through clinical symptoms, histopathology, and PET-CT imaging, while molecular markers for metastasis and the primary site are unknown. Here, we report the identification of multi-gene signatures for hepatic metastasis and primary sites through analyses on RNA-SEQ datasets of pancreatic and small intestinal NETs tissue samples. Relevant gene features, identified from the normalized RNA-SEQ data using the mRMRe algorithm, were used to develop seven Machine Learning models (LDA, RF, CART, k-NN, SVM, XGBOOST, GBM). Two multi-gene random forest (RF) models classified primary and metastatic samples with 100% accuracy in training and test cohorts and >90% accuracy in an independent validation cohort. Similarly, three multi-gene RF models identified the pancreas or small intestine as the primary site with 100% accuracy in training and test cohorts, and >95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned >98.42% and >87.42% accuracies on training and test cohorts, respectively. A robust molecular signature to predict liver metastasis or the primary site for GEP-NETs is reported for the first time and could complement the clinical management of GEP-NETs.

Pre-pregnancy gene expression signatures are associated with subsequent improvement/worsening of rheumatoid arthritis during pregnancy

BACKGROUND

While many women with rheumatoid arthritis (RA) improve during pregnancy and others worsen, there are no biomarkers to predict this improvement or worsening. In our unique RA pregnancy cohort that includes a pre-pregnancy baseline, we have examined pre-pregnancy gene co-expression networks to identify differences between women with RA who subsequently improve during pregnancy and those who worsen.

METHODS

Blood samples were collected before pregnancy (T0) from 19 women with RA and 13 healthy women enrolled in our prospective pregnancy cohort. RA improvement/worsening between T0 and 3rd trimester was assessed by changes in the Clinical Disease Activity Index (CDAI). Pre-pregnancy expression profiles were examined by RNA sequencing and differential gene expression analysis. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules correlated with the improvement/worsening of RA during pregnancy and to assess their functional relevance.

RESULTS

Of the 19 women with RA, 14 improved during pregnancy (RAimproved) while 5 worsened (RAworsened). At the T0 baseline, however, the mean CDAI was similar between the two groups. WGCNA identified one co-expression module related to B cell function that was significantly correlated with the worsening of RA during pregnancy and was significantly enriched in genes differentially expressed between the RAimproved and RAworsened groups. A neutrophil-related expression signature was also identified in the RAimproved group at the T0 baseline.

CONCLUSION

The pre-pregnancy gene expression signatures identified represent potential biomarkers to predict the subsequent improvement/worsening of RA during pregnancy, which has important implications for the personalized treatment of RA during pregnancy.

Targeting PTBP1 blocks glutamine metabolism to improve the cisplatin sensitivity of hepatocarcinoma cells through modulating the mRNA stability of glutaminase

Hepatocellular carcinoma (HCC) is a frequently diagnosed malignancy with a high mortality rate. Cisplatin (CDDP) is a widely applied anti-cancer drug. However, a large population of liver cancer patients developed CDDP resistance. The polypyrimidine tract binding protein (PTBP1) is an RNA-binding protein involving in progressions of diverse cancers. Here we report PTBP1 was significantly upregulated in liver tumors and cell lines. Silencing PTBP1 effectively sensitized HCC cells to CDDP. From the established CDDP-resistant HCC cell line (HepG2 CDDP Res), we observed that CDDP-resistant cells were more sensitive to CDDP under low glutamine supply compared with that in HCC parental cells. CDDP-resistant HCC cells displayed elevated glutamine metabolism rate. Consistently, PTBP1 promotes glutamine uptake and the glutamine metabolism key enzyme, glutaminase (GLS) expression. Bioinformatics analysis predicted that the 3'-UTR of GLS mRNA contained PTBP1 binding motifs which were further validated by RNA immunoprecipitation and RNA pull-down assays. PTBP1 associated with GLS 3'-UTR to stabilize GLS mRNA in HCC cells. Finally, we demonstrated that the PTBP1-promoted CDDP resistance of HCC cells was through modulating the GLS-glutamine metabolism axis. Summarily, our findings uncovered a PTBP1-mediated CDDP resistance pathway in HCC, suggesting that PTBP1 is a promisingly therapeutic target to overcome chemoresistance of HCC.

Knockdown of RFC4 inhibits the cell proliferation of nasopharyngeal carcinoma in vitro and in vivo

Nasopharyngeal carcinoma (NPC) is a malignant tumor that mainly occurs in East and Southeast Asia. Although patients benefit from the main NPC treatments (e.g., radiotherapy and concurrent chemotherapy), persistent and recurrent diseases still occur in some NPC patients. Therefore, investigating the pathogenesis of NPC is of great clinical significance. In the present study, replication factor c subunit 4 (RFC4) is a key potential target involved in NPC progression via bioinformatics analysis. Furthermore, the expression and mechanism of RFC4 in NPC were investigated in vitro and in vivo. Our results revealed that RFC4 was more elevated in NPC tumor tissues than in normal tissues. RFC4 knockdown induced G2/M cell cycle arrest and inhibited NPC cell proliferation in vitro and in vivo. Interestingly, HOXA10 was confirmed as a downstream target of RFC4, and the overexpression of HOXA10 attenuated the silencing of RFC4-induced cell proliferation, colony formation inhibition, and cell cycle arrest. For the first time, this study reveals that RFC4 is required for NPC cell proliferation and may play a pivotal role in NPC tumorigenesis.

fRNC: Uncovering the dynamic and condition-specific RBP-ncRNA circuits from multi-omics data

The RNA binding protein (RBP) and non-coding RNA (ncRNA) interacting networks are increasingly recognized as the main mechanism in gene regulation, and are tightly associated with cellular malfunction and disease. Here, we present fRNC, a systems biology tool to uncover the dynamic spectrum of RBP-ncRNA circuits (RNC) by integrating transcriptomics, interactomics and proteomics data. fRNC constructs the RBP-ncRNA network derived from CLIP-seq or PARE experiments. Given scoring on nodes and edges according to differential analysis of expression data, it finds an RNC containing global maximum significant RBPs and ncRNAs. Alternatively, it can also capture the locally maximum scoring RNC according to user-defined starting nodes with the greedy search. When compared with existing tools, fRNC can detect more accurate and robust sub-network with scalability. As shown in the cases of esophageal carcinoma, breast cancer and Alzheimer's disease, fRNC enables users to analyze the collective behaviors between RBP and the interacting ncRNAs, and reveal novel insights into the disease-associated processes. The fRNC R package is available at https://github.com/BioinformaticsSTU/fRNC.

Blocking lncRNA-SNHG16 sensitizes gastric cancer cells to 5-Fu through targeting the miR-506-3p-PTBP1-mediated glucose metabolism

BACKGROUND

Gastric cancer (GC) is a commonly occurring human malignancy. The 5-fluorouracil (5-Fu) is a first-line anti-gastric cancer agent. However, a large number of GC patients developed 5-Fu resistance. Currently, the roles and molecular mechanisms of the lncRNA-SNHG16-modulated 5-Fu resistance in gastric cancer remain elusive.

METHODS

Expressions of lncRNA, miRNA, and mRNA were detected by qRT-PCR and Western blot. RNA-RNA interaction was examined by RNA pull-down and luciferase assay. Cell viability and apoptosis rate under 5-Fu treatments were determined by MTT assay and Annexin V assay. The glycolysis rate of GC cells was evaluated by glucose uptake and ECAR.

RESULTS

Here, we report that SNHG16 as well as PTBP1, which is an RNA-binding protein, are positively associated with 5-Fu resistance to gastric cancer. SNHG16 and PTBP1 were significantly upregulated in gastric tumors and cell lines. Silencing SNHG16 or PTBP1 effectively sensitized GC cells to 5-Fu. Furthermore, glucose metabolism was remarkedly elevated in 5-Fu-resistant GC cells. Under low glucose supply, 5-Fu-resistant cells displayed higher vulnerability than parental GC cells. Bioinformatic analysis and luciferase assay demonstrated that SNHG16 downregulated miR-506-3p by sponging it to form a ceRNA network. We identified PTBP1 as a direct target of miR-506-3p in GC cells. RNA-seq results unveiled that PTBP1 positively regulated expressions of multiple glycolysis enzymes, including GLUT1, HK2, and LDHA. Bioinformatic analysis illustrated the 3'UTRs of glycolysis enzymes contained multiple PTBP1 binding sites, which were further verified by RNA pull-down and RNA immunoprecipitation assays. Consequently, we demonstrated that PTBP1 upregulated the mRNAs of glycolysis enzymes via promoting their mRNA stabilities. Finally, in vivo xenograft experiments validated that blocking the SNHG16-mediated miR-506-3p-PTBP1 axis effectively limited 5-Fu-resistant GC cell originated-xenograft tumor growth under 5-Fu treatments.

CONCLUSIONS

Our study demonstrates molecular mechanisms of the SNHG16-mediated 5-Fu resistance of GC cells through modulating the miR-506-3p-PTBP1-glucose metabolism axis, presenting a promising approach for anti-chemoresistance therapy.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data

In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.

Bone Marrow Mesenchymal Stem Cells (BMSCs) Promote the Metastasis of Thyroid Papillary Cancer by Inhibiting Poly-Pyrimidine Tract Binding Protein 1 (PTBP1)

We aimed to explore the mechanism underlying the role of bone marrow mesenchymal stem cells (BMSCs) in the invasion of papillary thyroid cancer (PTC) cells. BMSCs were co-cultured with PTC cells WRO or normal thyroid follicular epithelial cells T3TD followed by analysis of cell migration and proliferation by Transwell assay and MTT assay. Cells were transfected with shRNA or overexpression of PTBP1, followed by measuring cell proliferation and invasion and PTBP1 expression by RT-qPCR and Western blot. Co-cultivation with MSC promoted the malignant transformation of WRO, inhibited the RNA-binding protein PTBP1 and activation of GS3Kβ/Akt. In addition, silencing of PTBP1 accelerated cell invason and induced overexpression of EMT proteins, while overexpression of PTBP1 inhibited cell proliferation and migration. In conclusion, BMSCs might promote PTC invasion and metastasis by inhibiting PTBP1 expression, providing a novel insight into the treatment of PTC.

Identification of Potential Driver Genes and Pathways Based on Transcriptomics Data in Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. To identify AD-related genes from transcriptomics and help to develop new drugs to treat AD. In this study, firstly, we obtained differentially expressed genes (DEG)-enriched coexpression networks between AD and normal samples in multiple transcriptomics datasets by weighted gene co-expression network analysis (WGCNA). Then, a convergent genomic approach (CFG) integrating multiple AD-related evidence was used to prioritize potential genes from DEG-enriched modules. Subsequently, we identified candidate genes in the potential genes list. Lastly, we combined deepDTnet and SAveRUNNER to predict interaction among candidate genes, drug and AD. Experiments on five datasets show that the CFG score of GJA1 is the highest among all potential driver genes of AD. Moreover, we found GJA1 interacts with AD from target-drugs-diseases network prediction. Therefore, candidate gene GJA1 is the most likely to be target of AD. In summary, identification of AD-related genes contributes to the understanding of AD pathophysiology and the development of new drugs.

Construction of a Co-Expression Network for lncRNAs and mRNAs Related to Urothelial Carcinoma of the Bladder Progression

Carcinoma of urinary bladder is the most familiar cancer of the urinary tract, with the highest incidence in men. However, its prognosis and treatment have not improved significantly in the last 30 years. The main reason for this may be related to the alteration and regulation of genes. These alterations in genes that play a crucial role in cell cycle regulation may result in high-grade tumors and may alter drug sensitivity. Notably, the role of lncRNA in bladder cancer, especially the lncRNA-mRNA regulatory network, has not been fully elucidated. In this manuscript, we compared RNA sequencing (RNA-seq) data from 19 normal bladder tissues and 411 primary bladder tumor tissues using The Cancer Genome Atlas (TCGA) data bank, subjected differentially expressed mRNAs and lncRNAs to weighted gene co-expression network analysis, and screened out modules highly correlated with tumor progression. Subsequently, a lncRNA-mRNA co-expression network was built, and two key mRNAs were identified via COX regression analysis. Kaplan-Meier curve analysis revealed that the overall survival of sick people in the high-risk section was significantly shorter than those in the low-risk section. Therefore, this lncRNA-mRNA-based co-expression pattern may be used clinically to predict the prognosis of carcinoma of urinary bladder people. Our study not only provides a genetic target for carcinoma of urinary bladder therapy but also provides new ideas for people in the medical profession to discover the treatment of various tumors.

ACAN, MDFI, and CHST1 as Candidate Genes in Gastric Cancer: A Comprehensive Insilco Analysis

BACKGROUND

Gastric cancer (GC) is a complex disorder with an inadequate response to treatment. Although many efforts have been made to clarify the development of GC, the exact etiology and molecular mechanisms of this malignancy remain unclear. This study was designed to identify and characterize essential associated genes with GC to construct a prognostic model.

METHODS

In this Insilco study, the gene expression microarray dataset GSE122401 was downloaded from the Gene Expression Omnibus (GEO). The raw data were processed and quantile-normalized with the edgeR package of R ver.3.5.3. The module-trait relationship and hub-genes associated with GC were analyzed with Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Cluepedia and Enrichr Database. Finally, hub-genes were screened and validated by GEPIA online database.

RESULTS

According to the WGCNA results, the blue module was found to be strongly correlated with the GC (r=0.91, p-value=1e-57). DEGs analysis was performed by edgeR package of R and indicated a total of 47 genes as hub-genes. Verifying the hub-genes expression using GEPIA online database showed a significantly increased level of ACAN gene expression in primary cancer cell line compared to metastatic cell line. On the other hand, the expression of MDFI and CHST1 genes in primary cell lines were lower compared to metastatic cancer cell lines.

CONCLUSIONS

This study provides a framework of the co-expression gene modules ACAN, MDFI, and CHST1 as hub-genes. These hub-genes might offer candidate biomarkers to targeted therapy against GC. Further experiment validation and animal models are needed to reveal the exact mechanism of the above-mentioned genes in the pathogenesis and prognoses of GC.

Revealing the Role of lncRNA CCDC144NL-AS1 and LINC01614 in Gastric Cancer via Integrative Bioinformatics Analysis and Experimental Validation

Long non-coding RNAs (lncRNAs) are key regulators in the pathophysiology of gastric cancer, and lncRNAs have been regarded as potential biomarkers and therapeutic targets for gastric cancer. The present study performed the WGCNA analysis of the GSE70880 dataset and aimed to identify novel lncRNAs associated with gastric cancer progression. Based on the WGCNA, the lncRNAs and mRNA co-expression network were constructed. A total of four modules were identified and the eigengenes in different modules were involved in various key signaling pathways. Furthermore, the co-expression networks were constructed between the lncRNAs and mRNA; this leads to the identification of 6 modules, which participated in various cellular pathways. The survival analysis showed that high expression of CCDC144NL antisense RNA 1 (CCDC144NL-AS1) and LINC01614 was positively correlated with the poor prognosis of patients with gastric cancer. The in vitro validation results showed that CCDC144NL-AS1 and LINC01614 were both up-regulated in the gastric cancer cells. Silence of CCDC144NL-AS1 and LINC01614 both significantly suppressed the cell proliferation and migration of gastric cancer cells, and also promoted the chemosensitivity of gastric cancer cells to 5-fluorouracil. Collectively, our results suggested that the newly identified two lncRNAs (CCDC144NL-AS1 and LINC01614) may act as oncogenes in gastric cancer.

LncRNA ILF3-AS1 promotes cell migration, invasion and EMT process in hepatocellular carcinoma via the miR-628-5p/MEIS2 axis to activate the Notch pathway

BACKGROUND

Long non-coding RNAs (lncRNAs) are essential indicators for hepatocellular carcinoma. LncRNAs can exert the same functions as their antisense mRNAs. ILF3 is an oncogene in hepatocellular carcinoma. ILF3 divergent transcript (ILF3-AS1) is the antisense RNA of ILF3, and has been reported as an oncogene in various cancers.

AIMS

To explore the role of lncRNA ILF3-AS1 in malignant phenotypes of hepatocellular carcinoma cells.

METHODS AND RESULTS

RT-qPCR analysis revealed that ILF3-AS1 was significantly upregulated in hepatocellular carcinoma cells. The hepatocellular carcinoma cell viability was suppressed by silenced ILF3-AS1. Transwell and wound healing assays showed that ILF3-AS1 downregulation inhibited cell invasion and migration. The levels of proteins associated with epithelial-mesenchymal transition (EMT) process and the Notch pathway were detected by western blot analysis. Luciferase reporter, RNA pull down and RIP assays were used to investigate the relationship between ILF3-AS1 and downstream target genes. ILF3-AS1 competed with meis homeobox 2 (MEIS2) for miR-628-5p in hepatocellular carcinoma cells. ILF3-AS1 elevated the levels of key proteins on the Notch pathway. Rescue assays demonstrated that MEIS2 reversed the antitumor effects of silenced ILF3-AS1 on hepatocellular carcinoma. In vivo assays demonstrated that ILF3-AS1 silencing inhibited the hepatocellular carcinoma tumor growth.

CONCLUSIONS

ILF3-AS1 promoted hepatocellular carcinoma progression via the Notch pathway and miR-628-5p/MEIS2 axis.

A Novel Ferroptosis-Related Gene Risk Signature for Predicting Prognosis and Immunotherapy Response in Gastric Cancer

Background

Gastric cancer (GC) is the third leading cause of cancer death worldwide with complicated molecular and cellular heterogeneity. Iron metabolism and ferroptosis play crucial roles in the pathogenesis of GC. However, the prognostic role and immunotherapy biomarker potential of ferroptosis-related genes (FRGs) in GC still remains to be clarified.

Methods

We comprehensively analyzed the prognosis of different expression FRGs, based on gastric carcinoma patients in the TCGA cohort. The functional enrichment and immune microenvironment associated with these genes in gastric cancer were investigated. The prognostic model was constructed to clarify the relation between FRGs and the prognosis of GC. Meanwhile, the ceRNA network of FRGs in the prognostic model was performed to explore the regulatory mechanisms.

Results

Gastric carcinoma patients were classified into the A, B, and C FRGClusters with different features based on 19 prognostic ferroptosis-related differentially expressed genes in the TCGA database. To quantify the FRG characteristics of individual patients, FRGScore was constructed. And the research shows the GC patients with higher FRGScore had worse survival outcome. Moreover, thirteen prognostic ferroptosis-related differentially expressed genes (DEGs) were selected to construct a prognostic model for GC survival outcome with a superior accuracy in this research. And we also found that FRG RiskScore can be an independent biomarker for the prognosis of GC patients. Interestingly, GC patients with lower RiskScore had less immune dysfunction and were more likely to respond to immunotherapy according to TIDE value analysis. Finally, a ceRNA network based on FRGs in the prognostic model was analyzed to show the concrete regulation mechanisms.

Conclusions

The ferroptosis-related gene risk signature has a superior potent in predicting GC prognosis and acts as the biomarkers for immunotherapy, which may provide a reference in clinic.

Long non-coding RNA ILF3-AS1 facilitates hepatocellular carcinoma progression by stabilizing ILF3 mRNA in an m6A-dependent manner

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Increasing evidences have demonstrated that ILF3 antisense RNA 1 (ILF3-AS1) acts as an oncogenic long noncoding RNA (lncRNA) in several types of human cancers. However, the expression pattern, functional role and underlying mechanism of ILF3-AS1 in HCC remains largely unclear. Here, we found that ILF3-AS1 expression was significantly elevated in HCC tissues and also associated with prognosis of patients with HCC. Functional assays demonstrated that knockdown of ILF3-AS1 expression resulted in the suppression of proliferation, migration and invasion in HCC cells, whereas overexpression of ILF3-AS1 exerted opposite effects. Additionally, knockdown of IFL3-AS1 attenuated HCC tumorigenesis and metastasis in vivo. Mechanistically, ILF3-AS1 associated with ILF3 mRNA and inhibited its degradation. ILF3-AS1 increased ILF3 m⁶A level via recruiting N⁶-methyladenosine (m⁶A) RNA methyltransferase METTL3. Moreover, IFL3-AS1 enhanced the interaction between ILF3 mRNA and m⁶A reader IGF2BP1. Overall, our study revealed the function and mechanism of ILF3-AS1 in the malignant phenotypes of HCC cells, which provides a novel therapeutic target for HCC.

LINC00511 promotes gastric cancer progression by regulating SOX4 and epigenetically repressing PTEN to activate PI3K/AKT pathway

Gastric cancer (GC) serves as a common malignancy. Long non-coding RNAs (lncRNAs) have been proven to regulate many cancers, including GC. Long intergenic non-protein-coding RNA 511 (LINC00511) has been poorly studied in GC, but its detailed regulatory mechanism has not been identified. Here, LINC00511 was detected to be highly expressed in GC cells. Functional assays were conducted and uncovered that LINC00511 boosted cell proliferation, migration, stemness and EMT process while inhibiting the apoptosis of GC cells. From a series of mechanism experiments, it was found that at the transcriptional level, LINC00511 recruited EZH2 (enhancer of zeste 2 polycomb repressive complex 2 subunit) to the promoter of PTEN (phosphatase and tensin homolog) and facilitated methylation of PTEN promoter. LINC00511 epigenetically repressed PTEN to activate the PI3K/AKT pathway. Moreover, SRY-box transcription factor 4 (SOX4) activated the transcription of LINC00511. At the post-transcriptional level, LINC00511 sponged miR-195-5p to elevate SOX4 expression in GC cells. On the whole, the present study disclosed that SOX4-induced LINC00511 activated SOX4 via competing endogenous RNA (ceRNA) pattern and epigenetically repressed PTEN to activate PI3K/AKT pathway by recruiting EZH2, thus facilitating GC cell proliferation, migration and stemness while inhibiting GC cell apoptosis.

ILF3-AS1 promotes cell proliferation and inhibits cell apoptosis of breast cancer by binding with miR-4429 to upregulate RAB14

As one of the leading causes of cancer-related deaths among women, breast cancer accounts for a 30% increase of incidence worldwide since 1970s. Recently, increasing studies have revealed that the long non-coding RNA ILF3-AS1 is involved in the progression of various cancers. Nevertheless, the role of ILF3-AS1 in breast cancer remains largely unknown. In the present study, we found that ILF3-AS1 was highly expressed in breast cancer tissues and cells. ILF3-AS1 silencing inhibited breast cancer cell proliferation, migration and invasion, and promoted cell apoptosis. ILF3-AS1 bound with miR-4429 in breast cancer cells. Moreover, RAB14 was a downstream target of miR-4429, and miR-4429 expression was negatively correlated with RAB14 or ILF3-AS1 expression in breast cancer tissues. The result of rescue experiments demonstrated that overexpression of RAB14 can reverse the inhibitory effect of ILF3-AS1 knockdown on breast cancer cell proliferation, migration and invasion. Overall, ILF3-AS1 promotes the malignant phenotypes of breast cancer cells by interacting with miR-4429 to regulate RAB14, which might offer a new insight into the underlying mechanism of breast cancer.

Potential prognostic biomarkers related to immunity in clear cell renal cell carcinoma using bioinformatic strategy

The clear cell renal cell carcinoma (ccRCC) is the main pathological subtype of renal cell carcinoma. Immune system evasion, one hallmark of cancer, contributes to cancer cells in escaping from the attack of immune cells. In order to identify potential prognostic biomarkers in ccRCC patients and immune cells fraction, we collected and downloaded profiles from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. We obtained 2 modules significantly associated with tumor stage and immune cells; functional enrichment analysis showed that genes in the module ‘yellow’ were significantly enriched in proteins targeting to membrane and ribosome, as well as the oxidative phosphorylation pathway, while genes in the module ‘green’ mainly participate in molecular functions associated with immunity like activation of T cells. Four LncRNAs (LINC00472, AL590094.1, AL365203.3, and AC147651.3) and RPL27A and RPL22L1 in the module ‘yellow’ and two lncRNAs (LINC00426 and AC129507.2) and five protein-coding genes (CSF1, NOD2, ITGAE, CD7, and PDCD1) in the module ‘green’ represented independent prognostic values in patients with ccRCC. Expression of LINC0042, NOD2, CD7, and PDCD1 were significantly correlated with ratio of immune cells (like T cells CD8 and resting mast cells). LINC00426, with significant correlation with immune cell fraction, shows potential prognostic value in ccRCC patients. Our findings provide a strategy in exploring biomarkers with prognostic significance and significant association with the fraction of immune cells.

Weighted correlation network analysis identifies FN1, COL1A1 and SERPINE1 associated with the progression and prognosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most deadliest tumours worldwide, and its prognosis remains poor.

OBJECTIVE

This study aims to identify and validate hub genes associated with the progression and prognosis of GC by constructing a weighted correlation network.

METHODS

The gene co-expression network was constructed by the WGCNA package based on GC samples and clinical data from the TCGA database. The module of interest that was highly related to clinical traits, including stage, grade and overall survival (OS), was identified. GO and KEGG pathway enrichment analyses were performed using the clusterprofiler package in R. Cytoscape software was used to identify the 10 hub genes. Differential expression and survival analyses were performed on GEPIA web resources and verified by four GEO datasets and our clinical gastric specimens. The receiver operating characteristic (ROC) curves of hub genes were plotted using the pROC package in R. The potential pathogenic mechanisms of hub genes were analysed using gene set enrichment analysis (GSEA) software.

RESULTS

A total of ten modules were detected, and the magenta module was identified as highly related to OS, stage and grade. Enrichment analysis of magenta module indicated that ECM-receptor interaction, focal adhesion, PI3K-Akt pathway, proteoglycans in cancer were significantly enriched. The PPI network identified ten hub genes, namely COL1A1, COL1A2, FN1, POSTN, THBS2, COL11A1, SPP1, MMP13, COMP, and SERPINE1. Three hub genes (FN1, COL1A1 and SERPINE1) were finally identified to be associated with carcinogenicity and poor prognosis of GC, and all were independent risk factors for GC. The area under the curve (AUC) values of FN1, COL1A1 and SERPINE1 for the prediction of GC were 0.702, 0.917 and 0.812, respectively. GSEA showed that three hub genes share 15 common upregulated biological pathways, including hypoxia, epithelial mesenchymal transition, angiogenesis, and apoptosis.

CONCLUSION

We identified FN1, COL1A1 and SERPINE1 as being associated with the progression and poor prognosis of GC.

LncRNA ILF3-AS1 Promotes the Progression of Colon Adenocarcinoma Cells Through the miR-619-5p/CAMK1D Axis

Introduction

Colon adenocarcinoma (COAD) is the third most common tumor of the digestive tract. Recent studies reported that lncRNA’s abnormal expression might play a vital role in the occurrence and development of COAD.

Methods

In the present study, we investigated the expression of ILF3-AS1 in COAD cell lines, human normal colon epithelial cell line, patient tumor tissues and adjacent normal tissues by real-time quantitative PCR (RT-qPCR). Small interfering RNAs (siRNAs) were transfected into COAD cells to inhibit the expression of ILF3-AS1. The effects of ILF3-AS1 on cell proliferation, migration, invasion and apoptosis were measured by CCK-8 assay, transwell migration and invasion assay, and flow cytometry apoptosis assay, respectively. The direct binding of ILF3-AS1 and miR-619-5p/CAMK1D was validated by the luciferase reporter assay. The expression of CAMK1D and epithelial-mesenchymal transformation (EMT)-related proteins was detected by Western Blot analysis. Besides, in vivo experiments were conducted to demonstrate the oncogenic role of ILF3-AS1 in COAD.

Results

The results showed that the expression of ILF3-AS1 was significantly higher in COAD tissue than in normal adjacent samples, and this conclusion was confirmed in the available public datasets. After ILF3-AS1 knockdown, the proliferation of COAD cell lines SW480 and HT29 was significantly inhibited. At the same time, the apoptosis was increased, and the invasion and migration abilities were decreased. After further exploring the mechanisms, we found that ILF3-AS1 serves as a competitive endogenous RNA of mir-619-5p. It can bind to mir-619-5p and reduce its expression, thus regulating the target gene CAMK1D. In addition, we found that high expression of ILF3-AS1 was significantly associated with tumor grade, tumor size, and distant metastasis in COAD samples. In vivo experiments confirmed that ILF3-AS1 promotes tumor growth in COAD models.

Conclusion

The present study demonstrated that ILF3-AS1 plays an oncogenic role in COAD through regulating the miR-619-5p/CAMK1D axis, and inhibition of ILF3-AS1 may pave the way for COAD treatment.

Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data

Background

With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC).

Methods

Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis.

Results

A total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes (P < 0.01). Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC.

Conclusion

The gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.

Long noncoding RNA LINC01391 restrained gastric cancer aerobic glycolysis and tumorigenesis via targeting miR-12116/CMTM2 axis

Increasing studies indicate that long noncoding RNA (lncRNA) plays a critical role in aerobic glycolysis of various tumors. However, the contribution of lncRNA in gastric cancer (GC) cell glycolysis is still poorly understood. The objective of this research was to investigate the functional role and mechanism of lncRNA long intergenic non-protein coding RNA 1391 (LINC01391) in the aerobic glycolysis and tumorigenesis of GC. Here, we report that LINC01391 was low expressed in GC tissues and cell lines. LINC01391 overexpression hampered GC cell proliferation, migration, invasion and aerobic glycolysis, while LINC01391 knockdown demonstrated the opposite effects. LINC01391 overexpression delayed the tumor growth in vivo. Furthermore, LINC01391 interacted with miR-12116, and miR-12116 interacted with CMTM2 in GC cells. And miR-12116 and CMTM2 participated in the inhibitory effects of LINC01391 on cell migration, invasion and aerobic glycolysis in GC cells. LINC01391 restrained aerobic glycolysis and tumorigenesis of GC via targeting miR-12116/CMTM2 axis, which provides new insights into mechanism of GC progression.