Identification of key lncRNAs in the carcinogenesis and progression of colon adenocarcinoma by co-expression network analysis

Identification of Novel lncRNAs Related to Colorectal Cancer Through Bioinformatics Analysis

Long noncoding RNA (lncRNA) plays a critical role in cancer cell proliferation, invasion, metastasis, and chemoresistance. The current study introduces novel lncRNAs in colorectal cancer (CRC) through bioinformatics analysis. GSE134834 CRC-related microarray of Gene Expression Omnibus (GEO) was analyzed to identify differentially expressed genes (DEGs) in CRC samples against normal samples. Analysis revealed 6763 DEGs (p < 0.05 and |log fold change (FC)| ≥ 0.5) that include differentially expressed mRNA (DEmRNA) and differentially expressed long noncoding RNA (DElncRNA). Novel lncRNAs were identified, and to better understand the biological function of the identified lncRNAs, gene modules were constructed using weighted gene coexpression network analysis (WGCNA), and finally, two modules for lncRNAs were obtained. The coexpression modules with these lncRNAs were subjected to enrichment analysis in FunRich software to predict their functions through their coexpressed genes. Gene ontology results of modules related to novel lncRNA revealed they significantly enriched the cellular pathways regulation in cancer. The protein-protein interaction (PPI) network of novel lncRNAs-related modules was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and visualized using the Cytoscape software. Hub genes were screened from the PPI network by the CytoHubba plug-in of Cytoscape. The hub genes were MRTO4, CDK1, CDC20, RPF2, NOP58, NIFK, GTPBP4, BUB1, BUB1B, and BOP1 for the lightpink4 module and BYSL, RPS23 (ribosomal protein S23), RSL1D1 (ribosomal L1 domain containing 1), NAT10, NOP14, GNL2, MRPS12, NOL6 (nucleolar protein 6), IMP4, and RRP12 (ribosomal RNA processing 12 homolog) for the pink module. The expression levels of the top DEmRNA and module hub genes in CRC were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) database. Generally, our findings offer crucial insight into the hub genes and novel lncRNAs in the development of CRC by bioinformatics analysis, information that may prove useful in the identification of new biomarkers and treatment targets in CRC; however, more experimental investigation is required to validate the findings of the present study.

A human stomach cell type transcriptome atlas

BACKGROUND

The identification of cell type-specific genes and their modification under different conditions is central to our understanding of human health and disease. The stomach, a hollow organ in the upper gastrointestinal tract, provides an acidic environment that contributes to microbial defence and facilitates the activity of secreted digestive enzymes to process food and nutrients into chyme. In contrast to other sections of the gastrointestinal tract, detailed descriptions of cell type gene enrichment profiles in the stomach are absent from the major single-cell sequencing-based atlases.

RESULTS

Here, we use an integrative correlation analysis method to predict human stomach cell type transcriptome signatures using unfractionated stomach RNAseq data from 359 individuals. We profile parietal, chief, gastric mucous, gastric enteroendocrine, mitotic, endothelial, fibroblast, macrophage, neutrophil, T-cell, and plasma cells, identifying over 1600 cell type-enriched genes.

CONCLUSIONS

We uncover the cell type expression profile of several non-coding genes strongly associated with the progression of gastric cancer and, using a sex-based subset analysis, uncover a panel of male-only chief cell-enriched genes. This study provides a roadmap to further understand human stomach biology.

Identification and validation of 12 immune-related genes as a prognostic signature for colon adenocarcinoma

Colon adenocarcinoma (COAD) is a common malignant tumor of the digestive tract that threatens human health seriously. Thus, it is urgent to explore biomarkers that can be used to evaluate a patient's survival prognosis overall as a supplementary treatment. RNA-seq expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus, and Lasso and multivariate Cox regression analyses were used for developing the prognostic model. Finally, a nomogram comprising the prognostic model was established to evaluate survival overall. A risk model comprised of a total of 12 immune-related gene pairs was constructed. Further analysis revealed the model's independent prognostic ability in relation to other clinical characteristics. This model's nomogram could help clinicians choose personalized treatment for COAD patients. This model has significant potential to complement COAD's clinical identifying characteristics, and also provide new insights into the identification of colon cancer patients with a high risk of death.

Interactions Networks for Primary Heart Sarcomas

Personalized medicine incorporates genetic information into medical practice so as to optimize the management of chronic diseases. In rare diseases, such as heart cancer (incidence 0.0017-0.33%), this may be elusive. Ninety-five percent of the cases are due to secondary involvementwith the neoplasm originating in the lungs, breasts, kidney, blood, or skin. The clinical manifestations of heart tumors (benign or malignant) include heart failure, hypertension, and cardiac arrhythmias of varying severity, frequently resulting in blood vessel emboli, including strokes. This study aims to explain the pathophysiology and contribute to a P4 medicine model for use by cardiologists, pathologists, and oncologists. We created six gene/protein heart-related and tumor-related targets high-confidence interactomes, which unfold the main pathways that may lead to cardiac diseases (heart failure, hypertension, coronary artery disease, arrhythmias), i.e., the sympathetic nervous system, the renin-angiotensin-aldosterone axis and the endothelin pathway, and excludes others, such as the K oxidase or cytochrome P450 pathways. We concluded that heart cancer patients could be affected by beta-adrenergic blockers, ACE inhibitors, QT-prolonging antiarrhythmic drugs, antibiotics, and antipsychotics. Interactomes may elucidate unknown pathways, adding to patient/survivor wellness during/after chemo- and/or radio-therapy.

LncRNA AC079630.4 expression associated with the progression and prognosis in lung cancer

Mounting evidence has demonstrated the important role of long non-coding RNAs (lncRNAs) in the development and progression of lung cancer. In this study, we combined the methods of bioinformatics analysis and experimental validation, and aim to investigate the clinical significance and underlying mechanism of the novel lncRNA AC079630.4 in lung cancer. Finally, we found that AC079630.4 was significantly down-regulated in lung cancer tissues, including in its subtypes. Samples with low AC079630.4 expression had a more advanced pathological stage and a worse prognosis than those with high expression. In functional prediction, the KEGG pathway of apoptosis and the TRAIL signaling pathway were enriched in the samples with high AC079630.4 expression. In experimental validation, AC079630.4 over-expression could significantly inhibit the proliferation and clonality, and up-regulated the receptors of TRAIL (TRAIL-R1 and TRAIL-R2) in lung cancer cells. In conclusion, we adopted the methods of bioinformatics analysis and experimental validation, and identified a novel lncRNA of AC079630.4 as a tumor suppressor in lung cancer.

Identification of Hub Genes and Construction of a Transcriptional Regulatory Network Associated With Tumor Recurrence in Colorectal Cancer by Weighted Gene Co-expression Network Analysis

Tumor recurrence is one of the most important risk factors that can negatively affect the survival rate of colorectal cancer (CRC) patients. However, the key regulators dictating this process and their exact mechanisms are understudied. This study aimed to construct a gene co-expression network to predict the hub genes affecting CRC recurrence and to inspect the regulatory network of hub genes and transcription factors (TFs). A total of 177 cases from the GSE17536 dataset were analyzed via weighted gene co-expression network analysis to explore the modules related to CRC recurrence. Functional annotation of the key module genes was assessed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. The protein and protein interaction network was then built to screen hub genes. Samples from the Cancer Genome Atlas (TCGA) were further used to validate the hub genes. Construction of a TFs-miRNAs–hub genes network was also conducted using StarBase and Cytoscape approaches. After identification and validation, a total of five genes (TIMP1, SPARCL1, MYL9, TPM2, and CNN1) were selected as hub genes. A regulatory network of TFs-miRNAs-targets with 29 TFs, 58 miRNAs, and five hub genes was instituted, including model GATA6-MIR106A-CNN1, SP4-MIR424-TPM2, SP4-MIR326-MYL9, ETS1-MIR22-TIMP1, and ETS1-MIR22-SPARCL1. In conclusion, the identification of these hub genes and the prediction of the Regulatory relationship of TFs-miRNAs-hub genes may provide a novel insight for understanding the underlying mechanism for CRC recurrence.

Identifying miRNA modules associated with progression of keloids through weighted gene co-expression network analysis and experimental validation in vitro

Keloid is a type of skin fibroproliferative disease, characterized by excessive deposition of collagen in the extracellular matrix, myofibroblast activation and invasive growth to the surrounding normal skin tissue. However, the specific pathogenesis of keloids is not yet fully understood and existing treatment strategies are unsatisfied. It is therefore urgent to explore new biomarkers associated with its progression for keloids. In this study, the microarray dataset GSE113620 was downloaded from the Gene Expression Omnibus (GEO) database to screen out the differential expression of miRNAs (DEMs). The DEMs with large variance were applied to construct a weighted gene co-expression network to identify miRNA modules that are closely relevant to keloid progression. It is worth noting that miR-424-3p in the blue module (r = 0.98, p = 1e-18) is considered to be the ultimate target most relevant to keloid progression through co-expressed network analysis. Subsequently, the results of molecular biology experiments determine that miR-424-3p targeting Smad7 significantly enhanced the ability of cell proliferation, migration and collagen secretion after transfection with miR-424-3p mimic, while the apoptosis rate was significantly reduced. On the contrary, the miR-424-3p inhibitor performs the exact opposite function.

Identification of key lncRNAs in the tumorigenesis of intraductal pancreatic mucinous neoplasm by coexpression network analysis

Intraductal papillary mucinous neoplasm (IPMN) is an intraepithelial precancerous lesion of pancreatic ductal adenocarcinoma (PDAC) that progresses from adenoma to carcinoma, and long noncoding RNAs (lncRNA) might be involved in the tumorigenesis. In this study, we obtained the expression profiles of more than 4000 lncRNAs by probe reannotation of a microarray dataset. As a correlation network‐based systems biology method, weighted gene coexpression network analysis (WGCNA) was used to find clusters of highly correlated lncRNAs in the tumorigenesis of IPMN, which covered four stepwise stages from normal main pancreatic duct to invasive IPMN. In the most relevant module (R² = −0.75 and P = 5E‐05), three hub lncRNAs were identified (HAND2‐AS1, CTD‐2033D15.2, and lncRNA‐TFG). HAND2‐AS1 and CTD‐2033D15.2 were negatively correlated with the tumorigenesis (P in one‐way ANOVA test = 1.45E‐07 and 1.39E‐0.5), while lncRNA‐TFG were positively correlated with the tumorigenesis (P = 3.99E‐08). The validation set reached consistent results (P = 2.66E‐03 in HAND2‐AS1, 1.47E‐04 in CTD‐2033D15.2 and 6.23E‐08 in lncRNA‐TFG). In functional enrichment analysis, the target genes of microRNAs targeting also these lncRNAs were overlapped in multiple biological processes, pathways and malignant diseases including pancreatic cancer. In survival analysis, patients with higher expression of HAND2‐AS1‐targeted and CTD‐2033D15.2‐targeted microRNAs showed a significantly poorer prognosis in PDAC, while high expression of lncRNA‐TFG‐targeted microRNAs demonstrated an obviously better prognosis (log‐rank P < .05). In conclusion, by coexpression network analysis of the lncRNA profiles, three key lncRNAs were identified in association with the tumorigenesis of IPMN, and those lncRNAs might act as early diagnostic biomarkers or therapeutic targets in pancreatic cancer.

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

Gastric cancer (GC) is one of the most common types of human cancers worldwide. However, the detail mechanisms underlying GC progression remained to be investigated. The present study identified 2823 differently expressed mRNAs and 441 differently expressed lncRNAs in GC. WGCNA was conducted to identify highly correlated lncRNAs and mRNAs. Bioinformatics analysis observed that these dysregulated lncRNAs were significantly associated with the regulation of angiogenesis, cell division, cell-cell adhesion, blood vessel development, adaptive immune response, gastric acid secretion, immune response. Co-expression analysis identified ILF3-AS1 was a key lncRNA involved in regulating GC progression. Loss of function assays showed that knockdown of ILF3-AS1 significantly suppressed GC cell proliferation and metastasis. Mechanically, the results indicate that ILF3-AS1 could enhance PTBP3 expression as an miR-29a sponge, thereby promoting the proliferation and metastasis of GC cells. Our work suggests that the ILF3-AS1/miR-29a/PTBP3 axis may be a potential target for the clinical diagnosis and treatment of GC.

Weighted gene co-expression network analysis identified MYL9 and CNN1 are associated with recurrence in colorectal cancer

Colorectal cancer (CRC) is one of the most common carcinomas and the fourth leading cause of cancer-related death worldwide. One of the obstacles in the successful treatment of CRC is a high rate of recurrence. We aimed to construct weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes in association with recurrence in CRC patients. We firstly used the microarray data, GSE41258, to construct a co-expression network and identify gene modules. Furthermore, protein and protein interaction (PPI) network was also performed to screen hub genes. To validate the hub genes, an independent dataset GSE17536 was used for survival analyses. Additionally, another two databases were also performed to investigate the survival rates and expression levels of hub genes. Gene set enrichment analyses (GSEA) combined with gene ontology (GO) were performed to further explore function and mechanisms. In our study, the midnightblue module was identified to be significant, 15 hub genes were screened, four of which were identified as hub nodes in the PPI network. In the test dataset, we found higher expression of MYL9 and CNN1 were significantly associated with shorter survival time of CRC patients. GO analyses showed that MYL9 and CNN1 were enriched in “muscle system process” and “cytoskeletal protein binding”. GSEA found the two hub genes were enriched in “pathways in cancer” and “calcium signaling pathway”. In conclusion, our study demonstrated that MYL9 and CNN1 were hub genes associated with the recurrence of CRC, which may contribute to the improvement of recurrence-free survival time of CRC patients.

Identification and functional analysis of long non-coding RNAs in the synovial membrane of osteoarthritis patients

Osteoarthritis (OA), the most common chronic joint disease in the elderly, has become a significant economic burden for families and societies worldwide. Although treatments are continually improving, current drugs only target joint pain, with no effective therapies modifying OA progression. Long noncoding RNAs (lncRNAs), which have received increasing attention in recent years, are abnormally expressed in OA cartilage. In the present study, weighted coexpression network analysis (WGCNA) was applied to identify modules related to certain OA clinical traits. In total, 4404 coding genes and 161 lncRNAs were differentially expressed based on two OA expression profile data sets and normal control samples. Subsequently, 11 independent modules were acquired, and the green module, with a total of 49 hub genes, was identified as the most relevant to OA. These hub genes were validated using the GSE12021 data set. There was only one lncRNA among the hub genes, namely, NONHSAG034351. Thus, we further explored the function of NONHSAG034351‐related genes in the network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that NONHSAG034351‐associated genes are involved in the response to lipopolysaccharide, angiogenesis, tumour necrosis factor (TNF) signalling, and mitogen‐activated protein kinase (MAPK) signalling pathways. In conclusion, we identified modules through WGCNA related to OA clinical traits. NONHSAG034351, the only hub‐lncRNA, was downregulated in OA synovial tissue and might play a significant role in the pathological progression of this disease. Our findings have important clinical implications and could provide novel biomarkers that indicate the molecular mechanisms of OA and act as potential therapeutic targets.

Significance of this study

Long noncoding RNAs (lncRNAs) have been reported to be abnormally expressed in osteoarthritis (OA), which is the most common chronic joint disease among the elderly. In the present study, we report the expression profiles of lncRNAs in OA and the identification of modules through WGCNA related to OA clinical traits. NONHSAG034351, the only hub‐lncRNA identified to be downregulated in the synovial tissue of OA patients, might play a significant role in the pathological progression of OA. Furthermore, our findings provide novel biomarkers associated with the molecular mechanisms underlying OA pathogenesis, thus implying potential therapeutic targets with important clinical implications.

Prognostic and clinicopathological significance of long non-coding RNA UCA1 in colorectal cancer: Results from a meta-analysis

OBJECTIVE

Urothelial cancer-associated 1 (UCA1), an oncogenic long non-coding RNA, was aberrantly upregulated in colorectal cancer (CRC). This study aimed to further explore the clinical value of UCA1 in CRC.

METHODS

Eligible studies were retrieved by searching Pubmed, Embase, Cochrane Library, Web of Science, Chinese National Knowledge Infrastructure, and Wanfang databases. Pooled hazard ratio (HR) and odds ratio (OR) with 95% confidence interval (CI) were applied to assess the prognostic role and clinical significance of UCA1.

RESULTS

A total of 7 eligible studies with 775 cancer patients were recruited in the meta-analysis. The results showed that UCA1 overexpression was significantly correlated with poor overall survival in patients with CRC (HR = 2.25, 95% CI: 1.77-2.87, P < .001). There was also a significantly negative association between high UCA1 levels and tumor differentiation (OR = 2.84, 95% CI: 1.87-4.31, P < .001), lymph node metastasis (OR = 3.48, 95% CI: 2.24-5.41, P < .001), distant metastasis (OR = 2.67, 95% CI: 1.32-5.38, P = .006), tumor node metastasis stage (OR = 3.01, 95% CI: 2.16-4.18, P < .001), tumor invasion depth (OR = 2.18, 95% CI: 1.03-4.61, P = .04), and tumor size (OR = 2.27, 95% CI: 1.56-3.32, P < .001).

CONCLUSIONS

Our study revealed that UCA1 overexpression was associated with poor prognosis and more advanced clinicopathological features, suggesting that UCA1 may serve as an indicator for unfavorable outcome of patients with CRC.