Integrated analysis of competing endogenous RNA networks revealing five prognostic biomarkers associated with colorectal cancer

Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation

By 2030, it is anticipated that there will be 2.2 million new instances of colorectal cancer worldwide, along with 1.1 million yearly deaths. Therefore, it is critical to develop novel biomarkers that could help in CRC early detection. We performed an integrated analysis of four RNA-Seq data sets and TCGA datasets in this study to find novel biomarkers for diagnostic, prediction, and as potential therapeutic for this malignancy, as well as to determine the molecular mechanisms of CRC carcinogenesis. Four RNA-Seq datasets of colorectal cancer were downloaded from the Sequence Read Archive (SRA) database. The metaSeq package was used to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the string platform, and hub genes were identified using the cytoscape software. The gene ontology and KEGG pathway enrichment analysis were performed using enrichR package. Gene diagnostic sensitivity and its association to clinicopathological characteristics were demonstrated by statistical approaches. By using qRT-PCR, GUCA2A and COL3A1 were examined in colon cancer and rectal cancer. We identified 5037 differentially expressed genes, including (4752 upregulated, 285 downregulated) across the studies between CRC and normal tissues. Gene ontology and KEGG pathway analyses showed that the highest proportion of up-regulated DEGs was involved in RNA binding and RNA transport. Integral component of plasma membrane and mineral absorption pathways were identified as containing down-regulated DEGs. Similar expression patterns for GUCA2A and COL3A1 were seen in qRT-PCR and integrated RNA-Seq analysis. Additionally, this study demonstrated that GUCA2A and COL3A1 may play a significant role in the development of CRC.

Gibson T, McCain S, Stone BC, Gill T, Pickett BE. Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets

Background

Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions.

Methods

We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication.

Results

As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Identification of Key MicroRNAs and Genes between Colorectal Adenoma and Colorectal Cancer via Deep Learning on GEO Databases and Bioinformatics

Background

Deep learning techniques are gaining momentum in medical research. Colorectal adenoma (CRA) is a precancerous lesion that may develop into colorectal cancer (CRC) and its etiology and pathogenesis are unclear. This study aims to identify transcriptome differences between CRA and CRC via deep learning on Gene Expression Omnibus (GEO) databases and bioinformatics in the Chinese population.

Methods

In this study, three microarray datasets from the GEO database were used to identify the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) in CRA and CRC. The FunRich software was performed to predict the targeted mRNAs of DEMs. The targeted mRNAs were overlapped with DEGs to determine the key DEGs. Molecular mechanisms of CRA and CRC were evaluated using enrichment analysis. Cytoscape was used to construct protein-protein interaction (PPI) and miRNA-mRNA regulatory networks. We analyzed the expression of key DEMs and DEGs, their prognosis, and correlation with immune infiltration based on the Kaplan-Meier plotter, UALCAN, and TIMER databases.

Results

A total of 38 DEGs are obtained after the intersection, including 11 upregulated genes and 27 downregulated genes. The DEGs were involved in the pathways, including epithelial-to-mesenchymal transition, sphingolipid metabolism, and intrinsic pathway for apoptosis. The expression of has-miR-34c (P = 0.036), hsa-miR-320a (P = 0.045), and has-miR-338 (P = 0.0063) was correlated with the prognosis of CRC patients. The expression levels of BCL2, PPM1L, ARHGAP44, and PRKACB in CRC tissues were significantly lower than normal tissues (P < 0.001), while the expression levels of TPD52L2 and WNK4 in CRC tissues were significantly higher than normal tissues (P < 0.01). These key genes are significantly associated with the immune infiltration of CRC.

Conclusion

This preliminary study will help identify patients with CRA and early CRC and establish prevention and monitoring strategies to reduce the incidence of CRC.

Overexpression of LncRNA MNX1-AS1/PPFIA4 Activates AKT/HIF-1α Signal Pathway to Promote Stemness of Colorectal Adenocarcinoma Cells

Purpose. The purpose of this study was to explore the role of the lncRNA MNX1-AS1 and its related downstream signaling pathways in colorectal adenocarcinoma (COAD). Methods. COAD tissues and cells were prepared and treated with sh-MNX1-AS1, pcDNA-MNX1-AS1, sh-PPFIA4, LY29004, and their controls. CCK8 and colony formation assays were undertaken for evaluating cell proliferation. Tumor cell migratory ability was detected by transwell assay. Apoptosis detection was processed by YO-PRO-1/PI Staining. The regulated relationship between lncRNA MNX1-AS1 and PPFIA4 was confirmed by RIP-ChIP assay. Q-PCR was applied to detect genes related to tumor cell stemness, proliferation, migration, and apoptosis in each group. Finally, a xenograft tumor model was constructed to verify the result in vivo. Results. COAD patients with high expression of the lncRNA MNX1-AS1 have poor prognosis. LncRNA MNX1-AS1 promotes the stemness of COAD cells. PPFIA4 mediates lncRNA MNX1-AS1 expression and affects COAD cell stemness. LncRNA MNX1-AS1 accelerates proliferation and migration, while it suppresses apoptosis. LncRNA MNX1-AS1/PPFIA4 accelerates tumor growth in COAD model. LncRNA MNX1-AS1/PPFIA4 activates the downstream AKT/HIF-1α signaling pathway to promote COAD development. LY29004 significantly inhibits the tumorigenic ability of lncRNA MNX1-AS1 and PPFIA4. Conclusion. LncRNA MNX1-AS1/PPFIA4 activates AKT/HIF-1α signal pathway to promote the stemness of COAD cells, which could be a new target for COAD treatment.

Prognostic Roles of ceRNA Network-Based Signatures in Gastrointestinal Cancers

Gastrointestinal cancers (GICs) are high-incidence malignant tumors that seriously threaten human health around the world. Their complexity and heterogeneity make the classic staging system insufficient to guide patient management. Recently, competing endogenous RNA (ceRNA) interactions that closely link the function of protein-coding RNAs with that of non-coding RNAs, such as long non-coding RNA (lncRNA) and circular RNA (circRNA), has emerged as a novel molecular mechanism influencing miRNA-mediated gene regulation. Especially, ceRNA networks have proven to be powerful tools for deciphering cancer mechanisms and predicting therapeutic responses at the system level. Moreover, abnormal gene expression is one of the critical breaking events that disturb the stability of ceRNA network, highlighting the role of molecular biomarkers in optimizing cancer management and treatment. Therefore, developing prognostic signatures based on cancer-specific ceRNA network is of great significance for predicting clinical outcome or chemotherapy benefits of GIC patients. We herein introduce the current frontiers of ceRNA crosstalk in relation to their pathological implications and translational potentials in GICs, review the current researches on the prognostic signatures based on lncRNA or circRNA-mediated ceRNA networks in GICs, and highlight the translational implications of ceRNA signatures for GICs management. Furthermore, we summarize the computational approaches for establishing ceRNA network-based prognostic signatures, providing important clues for deciphering GIC biomarkers.

Long Non-coding RNA HOXA11-AS Facilitates Proliferation of Lung Adenocarcinoma Cells via Targeting the Let-7c-5p/IGF2BP1 Axis

Objective: This study investigates the relationship between the HOXA11-AS/let-7c-5p/IGF2BP1 regulatory axis and lung adenocarcinoma.

Methods: The expression levels of HOXA11-AS, let-7c-5p, and IGF2BP1 were evaluated in LUAD tissue and cell lines. Subcellular fractionation detection assay was adopted to verify the HOXA11-AS distribution in LUAD cells. The interaction relationship between let-7c-5p and HOXA11-AS or IGF2BP1 was validated by dual-luciferase reporter detection. In RNA binding protein immunoprecipitation assay, the binding relationship between HOXA11-AS and let-7c-5p was identified. The cell viability of transfected cells was tested by the Cell Counting Kit-8 assay. The mouse xenograft model was used to identify the effect of HOXA11-AS on tumor growth in vivo.

Results: Upregulation of lncRNA HOXA11-AS was found in LUAD, and suppression of HOXA11-AS could suppress the proliferative ability of LUAD cells. The let-7c-5p was expressed to be downregulated, which played an inhibitory role in LUAD cell proliferation. Let-7c-5p was negatively regulated by HOXA11-AS. HOXA11-AS promoted LUAD cell proliferation, while let-7c-5p had an inverse effect. Besides, IGF2BP1, regulated by let-7c-5p, had a positive relation with HOXA11-AS, while overexpression of IGF2BP1 could suppress the inhibition of silencing HOXA11-AS on LUAD cell proliferation. Experiments on mice confirmed that HOXA11-AS facilitated LUAD cell growth in vivo through regulating the let-7c-5p/IGF2BP1 axis.

Conclusion: HOXA11-AS promoted LUAD cell proliferation by targeting let-7c-5p/IGF2BP1, which could be potential molecular targets for LUAD.

Differential analysis revealing APOC1 to be a diagnostic and prognostic marker for liver metastases of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most malignant gastrointestinal cancers worldwide. The liver is the most important metastatic target organ, and liver metastasis is the leading cause of death in patients with CRC. Owing to the lack of sensitive biomarkers and unclear molecular mechanism, the occurrence of liver metastases cannot be predicted and the clinical outcomes are bad for liver metastases. Therefore, it is very important to identify the diagnostic or prognostic markers for liver metastases of CRC.

AIM

To investigate the highly differentially expressed genes (HDEGs) and prognostic marker for liver metastases of CRC.

METHODS

Data from three NCBI Gene Expression Omnibus (GEO) datasets were used to show HDEGs between liver metastases of CRC and tumour or normal samples. These significantly HDEGs of the three GEO datasets take the interactions. And these genes were screened through an online tool to explore the prognostic value. Then, TIMER and R package were utilized to investigate the immunity functions of the HDEGs and gene set enrichment analysis was used to explore their potential functions.

RESULTS

Based on the selection criteria, three CRC datasets for exploration (GSE14297, GSE41258, and GSE49355) were chosen. Venn diagrams were used to show HDEGs common to the six groups and 47 HDEGs were obtained. The HDEGs were shown by using STRING and Cytoscape software. Based on the TCGA database, APOC1 showed significantly different expression between N2 and N0, and N2 and N1. And there was also a significant difference in expression between T2 and T4, and between T2 and T3. In 20 paired CRC and normal tissues, quantitative real-time polymerase chain reaction illustrated that the APOC1 mRNA was strongly upregulated in CRC tissues (P = 0.014). PrognoScan and GEPIA2 revealed the prognostic value of APOC1 for overall survival and disease-free survival in CRC (P < 0.05). TIMER showed that APOC1 has a close relationship with immune infiltration (P < 0.05).

CONCLUSION

APOC1 is a biomarker that is associated with both the diagnosis and prognosis of liver metastases of CRC.

Differential Expression Analysis Revealing CLCA1 to Be a Prognostic and Diagnostic Biomarker for Colorectal Cancer

Colorectal cancer (CRC) is a common malignant tumor of the digestive tract and lacks specific diagnostic markers. In this study, we utilized 10 public datasets from the NCBI Gene Expression Omnibus (NCBI-GEO) database to identify a set of significantly differentially expressed genes (DEGs) between tumor and control samples and WGCNA (Weighted Gene Co-Expression Network Analysis) to construct gene co-expression networks incorporating the DEGs from The Cancer Genome Atlas (TCGA) and then identify genes shared between the GEO datasets and key modules. Then, these genes were screened via MCC to identify 20 hub genes. We utilized regression analyses to develop a prognostic model and utilized the random forest method to validate. All hub genes had good diagnostic value for CRC, but only CLCA1 was related to prognosis. Thus, we explored the potential biological value of CLCA1. The results of gene set enrichment analysis (GSEA) and immune infiltration analysis showed that CLCA1 was closely related to tumor metabolism and immune invasion of CRC. These analysis results revealed that CLCA1 may be a candidate diagnostic and prognostic biomarker for CRC.

Competitive endogenous RNA (ceRNA) regulation network of lncRNAs, miRNAs, and mRNAs in Wilms tumour

Background

Competitive endogenous RNAs (ceRNAs) have revealed a new mechanism of interaction between RNAs. However, an understanding of the ceRNA regulatory network in Wilms tumour (WT) remains limited.

Methods

The expression profiles of mRNAs, miRNAs and lncRNAs in Wilms tumour samples and normal samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database. The EdgeR package was employed to identify differentially expressed lncRNAs, miRNAs and mRNAs. Functional enrichment analyses via the ClusterProfile R package were performed, and the lncRNA–miRNA–mRNA interaction ceRNA network was established in Cytoscape. Subsequently, the correlation between the ceRNA network and overall survival was analysed.

Results

A total of 2037 lncRNAs, 154 miRNAs and 3609 mRNAs were identified as differentially expressed RNAs in Wilms tumour. Of those, 205 lncRNAs, 26 miRNAs and 143 mRNAs were included in the ceRNA regulatory network. The results of Gene Ontology (GO) analysis revealed that the differentially expressed genes (DEGs) were mainly enriched in terms related to response to mechanical stimuli, transcription factor complexes, and transcription factor activity (related to RNA polymerase II proximal promoter sequence-specific DNA binding). The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the DEGs were mainly enriched in pathways related to the cell cycle. The survival analysis results showed that 16 out of the 205 lncRNAs, 1 out of 26 miRNAs and 5 out of 143 mRNAs were associated with overall survival in Wilms tumour patients (P < 0.05).

Conclusions

CeRNA networks play an important role in Wilms tumour. This finding might provide effective, novel insights for further understanding the mechanisms underlying Wilms tumour.

Identification and Clinical Validation of a Novel 4 Gene-Signature with Prognostic Utility in Colorectal Cancer

Colorectal cancer (CRC) is a high burden disease with several genes involved in tumor progression. The aim of the present study was to identify, generate and clinically validate a novel gene signature to improve prediction of overall survival (OS) to effectively manage colorectal cancer. We explored The Cancer Genome Atlas (TCGA), COAD and READ datasets (597 samples) from The Protein Atlas (TPA) database to extract a total of 595 candidate genes. In parallel, we identified 29 genes with perturbations in > 6 cancers which are also affected in CRC. These genes were entered in cBioportal to generate a 17 gene panel with highest perturbations. For clinical validation, this gene panel was tested on the FFPE tissues of colorectal cancer patients (88 patients) using Nanostring analysis. Using multivariate analysis, a high prognostic score (composite 4 gene signature-DPP7/2, YWHAB, MCM4 and FBXO46) was found to be a significant predictor of poor prognosis in CRC patients (HR: 3.42, 95% CI: 1.71-7.94, p < 0.001 *) along with stage (HR: 4.56, 95% CI: 1.35-19.15, p = 0.01 *). The Kaplan-Meier analysis also segregated patients on the basis of prognostic score (log-rank test, p = 0.001 *). The external validation using GEO dataset (GSE38832, 122 patients) corroborated the prognostic score (HR: 2.7, 95% CI: 1.99-3.73, p < 0.001 *). Additionally, higher score was able to differentiate stage II and III patients (130 patients) on the basis of OS (HR: 2.5, 95% CI: 1.78-3.63, p < 0.001 *). Overall, our results identify a novel 4 gene prognostic signature that has clinical utility in colorectal cancer.

Clusterin silencing restores myoblasts viability and down modulates the inflammatory process in osteoporotic disease

Background

Targeting new molecular pathways leading to Osteoporosis (OP) and Osteoarthritis (OA) is a hot topic for drug discovery. Clusterin (CLU) is a glycoprotein involved in inflammation, proliferation, cell death, neoplastic disease, Alzheimer disease and aging. The present study focuses on the expression and the role of CLU in influencing the decrease of muscle mass and fiber senescence in OP-OA condition.

Methods

Vastus lateralis muscle biopsies were collected from 20 women with OP undergoing surgery for fragility hip fracture and 20 women undergoing arthroplasty for hip osteoarthritis.

Results

We found an overexpression of CLU in degenerated fibers in OP closely correlated with interleukin 6 (IL6) and histone H4 acetylation level. Conversely, in OA muscle tissues we observed a weak expression of CLU but no nuclear histone H4 acetylation. Ex vivo studies on isolated human myoblasts confirmed CLU overexpression in OP as compared to OA (p < 0.001). CLU treatment of isolated OP and OA myoblasts showed: modulation of proliferation, morphological changes, increase of histone H4 acetylation and induction of myogenin (MYOG) activation in OP myoblast only. In OP condition, functional knockdown of CLU by siRNA restores proliferative myoblasts capability and tissue damage repair, carried out by an evident upregulation of Transglutaminase 2 (TGM2). We also observed downmodulation of CX3CR1 expression with consequent impairing of the inflammatory infiltrate recruitment.

Conclusions

Results obtained suggest a potential role of CLU in OP by influencing myoblasts terminal differentiation, epigenetic regulation of muscle cell differentiation and senescence. Moreover, CLU silencing points out its role in the modulation of tissue damage repair and inflammation, proposing it as a new diagnostic marker for muscle degeneration and a potential target for specific therapeutic intervention in OP related sarcopenia.