Long noncoding RNAs predict the survival of patients with colorectal cancer as revealed by constructing an endogenous RNA network using bioinformation analysis

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

BACKGROUND

Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods.

RESULTS

Therefore, we present an innovative Node-Adaptive Graph Transformer model for predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. NAGTLDA achieved an average AUC of 0.9531 and AUPR of 0.9537 significantly higher than state-of-the-art methods in 5-fold cross validation. We perform case studies on 4 diseases; 55 out of 60 associations between lncRNAs and diseases have been validated in the literatures. The results demonstrate the enormous potential of the graph Transformer structure to incorporate graph structural information for uncovering lncRNA-disease unknown correlations.

CONCLUSIONS

Our proposed NAGTLDA model can serve as a highly efficient computational method for predicting biological information associations.

Bornyl acetate extracted from Sharen () inhibits proliferation, invasion and induces apoptosis by suppressing phosphatidylinositol-3-kinase/protein kinase B signaling in colorectal cancer

OBJECTIVE

To investigate the antitumor effects of bornyl acetate (BA) isolated from Sharen (Fructus Amomi) in colorectal cancer (CRC) and the underlying mechanisms.

METHODS

SW480 and HT29 cells were treated with increasing doses of BA in order to determine its antitumor effects in vitro. Cell viability, colony formation, cell cycle, and apoptosis as well as migration and invasion were assessed using various assays. In addition, the in vivo antitumor effects of BA were assessed using a xenograft mouse model. We then assessed the mechanism of action of BA by conducting pathway activator-mediated rescue experiments and assessed the protein levels by Western blot analysis.

RESULTS

BA showed anti-CRC tumor activities in vitro by suppressing cell proliferation and colony formation, inducing apoptosis, blocking cell cycle, and inhibiting migration and invasion. These effects were mediated via suppression of the phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) pathway. In the tumor xenograft experiment, BA was found to repress tumor growth in vivo with low toxicity.

CONCLUSIONS

The results demonstrated that BA exerts antitumor effects by suppressing the PI3K/AKT pathway, with low toxicity. Thus, BA might be a potential novel therapeutic agent for CRC.

Host gene and its guest: short story about relation of long-noncoding MIR31HG transcript and microRNA miR-31

Epigenetics is the changes in a cellular phenotype without changes in the genotype. This term is not limited only to the modification of chromatin and DNA but also relates to some RNAs, like non-coding RNAs (ncRNAs), both short and long RNAs (lncRNAs) acting as molecular modifiers. Mobile RNAs, as a free form or encapsulated in exosomes, can regulate neighboring cells or be placed in distant locations. It underlines the vast capacity of ncRNAs as epigenetic elements of transmission information and message of life. One of the amazing phenomena is long non-coding microRNA-host-genes (lnc-MIRHGs) whose processed transcripts function as lncRNAs and also as short RNAs named microRNAs (miRNAs). MIR31HG functions as a modulator of important biological and cellular processes including cell proliferation, apoptosis, cell cycle regulation, EMT process, metastasis, angiogenesis, hypoxia, senescence, and inflammation. However, in most cases, the role of MIR31HG is documented only by one study and there is a lack of exact description of molecular pathways implicated in these processes, and for some of them, such as response to irradiation, no studies have been done. In this review, MIR31HG, as an example of lnc-MIRHGs, was described in the context of its known function and its potential uses as a biomarker in oncology.

Identification of a prognostic classifier based on EMT-related lncRNAs and the function of LINC01138 in tumor progression for lung adenocarcinoma

Purpose: This study aimed to develop a prognostic indicator based on epithelial-mesenchymal transition (EMT)-related long noncoding RNAs (lncRNAs) and explore the function of EMT-related lncRNAs in malignant progression in lung adenocarcinoma (LUAD).

Materials and methods: A LUAD dataset was acquired from The Cancer Genome Atlas (TCGA) to identify prognostic EMT-related lncRNAs via differential expression analysis and univariate Cox regression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was utilized for variable selection and model construction. The EMT-related prognostic index (ERPI) was calculated according to the model and served as a classifier to divide LUAD individuals into high-ERPI and low-ERPI groups. A nomogram incorporating ERPI and clinicopathological variables was constructed. TCGA-LUAD, GSE50081, and GSE31210 were used to test the predictive capacity of the ERPI and nomogram. The characteristics of the tumor microenvironment (TME) were evaluated via the ESTIMATE, TIMER, and ssGSEA algorithms. Gene set variation analysis (GSVA) and ssGSEA were used to annotate the functions of the high-ERPI and low-ERPI groups. CCK8, transwell assay, wound-healing assay, and clone formation assay were conducted to clarify the biological functions of prognostic EMT-related lncRNAs.

Results: Ninety-seven differentially expressed EMT-related lncRNAs were identified, 15 of which were related to overall survival (OS). A prognostic signature was constructed based on 14 prognostic EMT-related lncRNAs to calculate the ERPI of each patient, and the predictive ability of ERPI was verified in TCGA, GSE50081, and GSE31210. The low-ERPI group survived longer and had a lower percentage of patients in advanced stage than the high-ERPI group. The nomogram had the highest predictive accuracy, followed by ERPI and stage. Patients with low ERPI had higher infiltration degree of immune cells and stronger immune responses than those with high ERPI. A series of in vitro experiments demonstrated that knockdown of LINC01138 dampened variability, proliferation, and motility of A549 and H460 cells.

Conclusion: Our study developed a prognostic classifier with robust prognostic performance and clarified the biological functions of LINC01138 in LUAD, aiding in making individual treatments for patients with LUAD and dissecting the mechanism of oncogenesis.

HOTAIR/miR-1277-5p/ZEB1 axis mediates hypoxia-induced oxaliplatin resistance via regulating epithelial-mesenchymal transition in colorectal cancer

The hypoxic microenvironment contributes to the chemoresistance of many malignant tumors including colorectal cancer (CRC). Accumulating studies have indicated that long non-coding RNAs (lncRNAs) play important roles in chemotherapy resistance. In this study, we aimed to determine the effect of lncRNAs in hypoxia-mediated resistance in CRC and its potential mechanism. Here, we discovered that hypoxia-induced oxaliplatin resistance and HOX transcript antisense RNA (HOTAIR) expression was increased in hypoxia-treated CRC cell lines and CRC tumors. Knockdown of HOTAIR by siRNA reduced the viability and proliferation of CRC cells treated with oxaliplatin and reversed hypoxia-induced resistance. Mechanically, we found that HOTAIR modulates zinc finger E-box binding homeobox 1 (ZEB1) expression by negative regulations of miR-1277-5p. When miR-1277-5p was silenced, knockdown of HOTAIR was unable to reduce the oxaliplatin resistance in CRC cells. In mouse models of CRC, HOTAIR knockdown markedly inhibited the tumor growth when treated with oxaliplatin. Thus, HOTAIR/miR-1277-5p/ZEB1 axis appears a promising therapeutic target for improving the oxaliplatin efficacy in CRC.

LINC00488 Induces Tumorigenicity in Retinoblastoma by Regulating microRNA-30a-5p/EPHB2 Axis

OBJECTIVE

LINC00488 confers oncogenic activity in the progression of some tumors. Hence, the target of the study was about to specify LINC00488-mediated network in retinoblastoma (RB).

METHODS

LINC00488 expression was tested in RB clinical tissues. siRNA targeting LINC00488 or miR-30a-5p mimic was introduced into RB cell line (Y79) to observe cellular biological functions. The relationship between LINC00488, miR-30a-5p and EPHB2 was verified. Afterward, the role of miR-30a-5p involved in RB through targeted regulation of EPHB2 was probed in vitro and in vivo.

RESULTS

LINC00488 was induced in RB tissue and cells. LINC00488 knockdown or miR-30a-5p upregulation depressed the malignant activities of Y79 cells. LINC00488 could sponge miR-30a-5p that targeted EPHB2. EPHB2, and EPHB2 overexpression counteracted miR-30a-5p restoration-induced inhibition of Y79 cell development in vitro and in vivo.

CONCLUSION

LINC00488 induces tumorigenicity in RB by binding to miR-30a-5p to target EPHB2, which may offer a new clue of RB treatment from an lncRNA-miRNA-mRNA network.

C5orf66 rs4976270/rs639933 Are Associated with Colorectal Cancer Risk in Southern Chinese Han Population: A Case-Control Study

BACKGROUND

Colorectal cancer (CRC) is one of the common malignant tumors, with high mortality and poor prognosis. Our study aimed to determine the association between the long noncoding RNA (LncRNA) C5orf66 polymorphism and CRC risk in southern Chinese Han population.

METHOD

Using the experimental design of "case-control" study (512 cases and 513 controls), we selected 4 candidate single-nucleotide polymorphisms (SNPs) of C5orf66. All candidate SNPs were genotyped by Agena MassARRAY. Logistic regression was used to analyze the association between SNPs and CRC risk. Then, we used false-positive report probability analysis to detect whether the significant result is just a chance or noteworthy observation. Multi-factor dimensionality reduction was used to analyze the interaction of "SNP-SNP" in CRC risk.

RESULTS

Our results showed that C5orf66 SNPs rs4976270 (odds ratio [OR] = 1.69, p = 0.021) and rs639933 (OR = 1.67, p = 0.024) were, respectively, associated with increasing CRC risk in the southern Chinese Han population. Stratified analysis showed that rs4976270 and rs639933 were significantly associated with an increased risk of CRC in subgroups (>60 years, body mass index ≤24 and drinking) under multiple genetic models. In addition, rs254563 and rs647161 also had potential association with CRC risk in subgroups (BMI ≤24 and drinking). Finally, haplotype analysis results showed that haplotype "TA" was significantly associated with increased CRC risk (OR = 1.21, confidence interval = 1.47-2.02, p = 0.043).

CONCLUSION

Our study provides a new idea for the risk assessment of CRC. LncRNA C5orf66 SNPs have a certain association with CRC risk in the southern Chinese Han population.

Constructing a novel competing Endogenous RNAs network based on NR3C1 and X-linked inhibitor of apoptosis protein genes reveals potential prognostic biomarkers in colorectal cancer

Background:

Long noncoding RNAs (lncRNAs) have been recognized as the main modulatory molecules in various cancers and perform as competing endogenous RNAs (ceRNAs). The nuclear hormone receptor superfamily of ligand-activated transcription factors (NR3C1) regulates numerous proliferative and metabolic processes such as tumorigenesis and metabolic diseases. Furthermore, X-linked inhibitor of apoptosis protein (XIAP) belongs to a family of the inhibitors of apoptosis proteins, is located downstream of the glucocorticoid receptor (GR or NR3C1) pathway, and cooperates with GR to suppress apoptosis. However, the underlying mechanisms of NR3C1 and XIAP in colorectal cancer (CRC) remain mainly unclear. This research aims to clarify the potential RNA biomarkers and to construct a novel ceRNA network in CRC.

Materials and Methods:

Multistep bioinformatics methods such as Lnc2cancer and miRDB databases were applied to identify candidate lncRNAs and miRNAs. The interaction energy between lncRNAs, NR3C1, and XIAP genes was analyzed by the LncRRIsearch database. Plus, microRNAs and lncRNA were evaluated via the Diana tools database to select microRNAs with the most binding scores. Quantitative reverse transcription–polymerase chain reaction (QRT-PCR) was applied to verify RNA molecules’ expression levels and their association with the clinicopathological factors in 30 CRC tissues compared to 30 adjacent tissues.

Results:

QRT-PCR showed upregulation of KCNQ1OT1, NR3C1, and XIAP and downregulation of miR-421. The ceRNA network was constructed with 17 lncRNAs, 2 mRNAs, and 42 miRNAs. Thus, we explained the potential interactions between KCNQ1OT1 and miR-421 with NR3C1 and XIAP genes.

Conclusion:

Our study represents potential prognostic biomarkers and a new ceRNA network for further study in CRC.

Potential biomarkers and lncRNA-mRNA regulatory networks in invasive growth hormone-secreting pituitary adenomas

PURPOSE

Growth hormone-secreting pituitary adenomas (GH-PAs) are common subtypes of functional PAs. Invasive GH-PAs play a key role in restricting poor outcomes. The transcriptional changes in GH-PAs were evaluated.

METHODS

In this study, the transcriptome analysis of six different GH-PA samples was performed. The functional roles, co-regulatory network, and chromosome location of differentially expressed (DE) genes in invasive GH-PAs were explored.

RESULTS

Bioinformatic analysis revealed 101 DE mRNAs and 70 DE long non-coding RNAs (lncRNAs) between invasive and non-invasive GH-PAs. Functional enrichment analysis showed that epithelial cell differentiation and development pathways were suppressed in invasive GH-PAs, whereas the pathways of olfactory transduction, retinol metabolism, drug metabolism-cytochrome P450, and metabolism of xenobiotics by cytochrome P450 had an active trend. In the protein-protein interaction network, 11 main communities were characterized by cell- adhesion, -motility, and -cycle; transport process; phosphorus and hormone metabolic processes. The SGK1 gene was suggested to play a role in the invasiveness of GH-PAs. Furthermore, the up-regulated genes OR51B6, OR52E4, OR52E8, OR52E6, OR52N2, MAGEA6, MAGEC1, ST8SIA6-AS1, and the down-regulated genes GAD1-AS1 and SPINT1-AS1 were identified in the competing endogenous RNA network. The RT-qPCR results further supported the aberrant expression of those genes. Finally, the enrichment of DE genes in chromosome 11p15 and 12p13 regions were detected.

CONCLUSION

Our findings provide a new perspective for studies evaluating the underlying mechanism of invasive GH-PAs.

Competing Endogenous RNA in Colorectal Cancer: An Analysis for Colon, Rectum, and Rectosigmoid Junction

BACKGROUND

Colorectal cancer (CRC) is a heterogeneous cancer. Its treatment depends on its anatomical site and distinguishes between colon, rectum, and rectosigmoid junction cancer. This study aimed to identify diagnostic and prognostic biomarkers using networks of CRC-associated transcripts that can be built based on competing endogenous RNAs (ceRNA).

METHODS

RNA expression and clinical information data of patients with colon, rectum, and rectosigmoid junction cancer were obtained from The Cancer Genome Atlas (TCGA). The RNA expression profiles were assessed through bioinformatics analysis, and a ceRNA was constructed for each CRC site. A functional enrichment analysis was performed to assess the functional roles of the ceRNA networks in the prognosis of colon, rectum, and rectosigmoid junction cancer. Finally, to verify the ceRNA impact on prognosis, an overall survival analysis was performed.

RESULTS

The study identified various CRC site-specific prognosis biomarkers: hsa-miR-1271-5p, NRG1, hsa-miR-130a-3p, SNHG16, and hsa-miR-495-3p in the colon; E2F8 in the rectum and DMD and hsa-miR-130b-3p in the rectosigmoid junction. We also identified different biological pathways that highlight differences in CRC behavior at different anatomical sites, thus reinforcing the importance of correctly identifying the tumor site.

CONCLUSIONS

Several potential prognostic markers for colon, rectum, and rectosigmoid junction cancer were found. CeRNA networks could provide better understanding of the differences between, and common factors in, prognosis of colon, rectum, and rectosigmoid junction cancer.

Long non-coding RNAs in colorectal cancer: Novel oncogenic mechanisms and promising clinical applications

Colorectal cancer (CRC) is the third most common malignancy and ranks as the second leading cause of cancer-related deaths worldwide. Despite the improvements in CRC diagnosis and treatment approaches, a considerable proportion of CRC patients still suffers from poor prognosis due to late disease detections and lack of personalized disease managements. Recent evidences have not only provided important molecular insights into their mechanistic behaviors but also indicated that identification of cancer-specific long non-coding RNAs (LncRNAs) could benefit earlier disease detections and improve treatment outcomes in patients suffering from CRC. LncRNAs have raised extensive attentions as they participate in various hallmarks of CRC. The mechanistic evidence gleaned in the recent decade clearly reveals that lncRNAs exert their oncogenic roles by regulating autophagy, epigenetic modifications, enhancing stem phenotype and modifying tumor microenvironment. In view of their pleiotropic functional roles in malignant progression, and their frequently dysregulated expression in CRC patients, they have great potential to be reliable diagnostic and prognostic biomarkers, as well as therapeutic targets for CRC. In the present review, we will focus on the oncogenic roles of lncRNAs and related mechanisms in CRC as well as discuss their clinical potential in the early diagnosis, prognostic prediction and therapeutic translation in patients with this malignancy.

Construction of a long noncoding RNA-based competing endogenous RNA network and prognostic signatures of left- and right-side colon cancer

Background

Cancers located on the right and left sides of the colon have distinct clinical and molecular characteristics. This study aimed to explore the regulatory mechanisms of location-specific long noncoding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) in colon cancer and identify potential prognostic biomarkers.

Method

Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) between right- and left-side colon cancers were identified by comparing RNA sequencing profiles. Functional enrichment analysis was performed for the DEGs, and a ceRNA network was constructed. Associations between DELs and patient survival were examined, and a DEL-based signature was constructed to examine the prognostic value of these differences. Clinical colon cancer tissues and Gene Expression Omnibus (GEO) datasets were used to validate the results.

Results

We identified 376 DELs, 35 DEMs, and 805 DEGs between right- and left-side colon cancers. The functional enrichment analysis revealed the functions and pathway involvement of DEGs. A ceRNA network was constructed based on 95 DEL–DEM–DEG interactions. Three DELs (LINC01555, AC015712, and FZD10-AS1) were associated with the overall survival of patients with colon cancer, and a prognostic signature was established based on these three DELs. High risk scores for this signature indicated poor survival, suggesting that the signature has prognostic value for colon cancer. Examination of clinical colon cancer tissues and GEO dataset analysis confirmed the results.

Conclusion

The ceRNA regulatory network suggests roles for location-specific lncRNAs in colon cancer and allowed the development of an lncRNA-based prognostic signature, which could be used to assess prognosis and determine treatment strategies in patients with colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01901-3.

Comprehensive analyses of correlation and survival reveal informative lncRNA prognostic signatures in colon cancer

BACKGROUND

Colon cancer is a commonly worldwide cancer with high morbidity and mortality. Long non-coding RNAs (lncRNAs) are involved in many biological processes and are closely related to the occurrence of colon cancer. Identification of the prognostic signatures of lncRNAs in colon cancer has great significance for its treatment.

METHODS

We first identified the colon cancer-related mRNAs and lncRNAs according to the differential analysis methods using the expression data in TCGA. Then, we performed correlation analysis between the identified mRNAs and lncRNAs by integrating their expression values and secondary structure information to estimate the co-regulatory relationships between the cancer-related mRNAs and lncRNAs. Besides, the competing endogenous RNA regulation network based on co-regulatory relationships was constructed to reveal cancer-related regulatory patterns. Meanwhile, we used traditional regression analysis (univariate Cox analysis, random survival forest analysis, and lasso regression analysis) to screen the cancer-related lncRNAs. Finally, by combining the identified colon cancer-related lncRNAs according to the above analyses, we constructed a risk prognosis model for colon cancer through multivariate Cox analysis and also validated the model in the colon cancer dataset in TCGA cohorts.

RESULTS

Six lncRNAs were found highly correlated with the overall survival of colon cancer patients, and a risk prognosis model based on them was constructed to predict the overall survival of colon cancer patients. In particular, EVX1-AS, ZNF667-AS1, CTC-428G20.6, and CTC-297N7.9 were first reported to be related to colon cancer by using our model, among which EVX1-AS and ZNF667-AS1 have been predicted to be related to colon cancer in LncRNADisease database.

CONCLUSIONS

This study identified the potential regulatory relationships between lncRNAs and mRNAs by integrating their expression values and secondary structure information and presented a significant 6-lncRNA risk prognosis model to predict the overall survival of colon cancer patients.

Advances in Understanding the LncRNA-Mediated Regulation of the Hippo Pathway in Cancer

Long noncoding RNAs (lncRNAs) are a class of RNA molecules that are longer than 200 nucleotides and cannot encode proteins. Over the past decade, lncRNAs have been defined as regulatory elements of multiple biological processes, and their aberrant expression contributes to the development and progression of various malignancies. Recent studies have shown that lncRNAs are involved in key cancer-related signaling pathways, including the Hippo signaling pathway, which plays a prominent role in controlling organ size and tissue homeostasis by regulating cell proliferation, apoptosis, and differentiation. However, dysregulation of this pathway is associated with pathological conditions, especially cancer. Accumulating evidence has revealed that lncRNAs can modulate the Hippo signaling pathway in cancer. In this review, we elaborate on the role of the Hippo signaling pathway and the advances in the understanding of its lncRNA-mediated regulation in cancer. This review provides additional insight into carcinogenesis and will be of great clinical value for developing novel early detection and treatment strategies for this deadly disease.

Promising long noncoding RNA DLX6-AS1 in malignant tumors

Although its diagnosis and treatment have greatly improved in recent decades, cancer remains the major cause of death worldwide. Thus, there is an urgent need to find novel biomarkers and therapeutic targets to improve efficiency of diagnosis and treatment of patients with cancer. Long noncoding RNAs (lncRNAs), a new class of noncoding RNAs (ncRNAs), have been found to play a salient role in human tumorigenesis and progression. Distal-less homeobox 6 antisense RNA 1 (DLX6-AS1) is a novel lncRNA with aberrant expression in various cancers tissues and cell lines compared with nontumor tissues and normal cell lines. Importantly, DLX6-AS1 is closely associated with tumor cell proliferation, apoptosis, invasion, and migration. Patients with high DLX6-AS1 expression often had poorer prognosis than those with low expression. The oncogenicity of DLX6-AS1 mainly (indirectly or indirectly) interacts with targeting genes, and then regulates downstream genes and signaling pathways. Together with the findings of animal model studies, these data suggest that DLX6-AS1 may serve as a feasible predictor or therapeutic target in different cancers. Herein, we summarize the main findings concerning the function and molecular mechanisms of DLX6-AS1 to identify a molecular basis for future clinical application.

Integrated analysis of a competing endogenous RNA network reveals an 11-lncRNA prognostic signature in ovarian cancer

Long noncoding RNA (lncRNA) can function as a competing endogenous RNA (ceRNA) involved in tumor initiation and progression. However, the prognostic roles of lncRNAs in the integrated analysis of the ceRNA network in ovarian cancer (OVC) are still lacking. This study aimed to identify lncRNAs associated with the prognosis of OVC. Differential expression analysis and WGCNA were used to screen OVC-specific RNAs. A lncRNA-miRNA-mRNA regulatory network consisting of 201 lncRNAs, 85 miRNA and 146 mRNAs was constructed, and functional enrichment and protein-protein network analyses were performed. Then, the OVC-specific RNAs were submitted to Cox regression analysis. Twelve differentially expressed lncRNAs and mRNAs were identified as significantly associated with OS of OVC patients. Meanwhile, 11 lncRNAs (including C4A-AS1, LINC02408, LINC00488) were established as prognostic risk formulas. The low-risk group had better OS and DFS than the high-risk group (P <0.01). Univariate and multivariate Cox regression analyses revealed the 11-lncRNA risk score as an independent prognostic factor. A prognostic nomogram was developed based on independent prognostic factors. Our data provide evidence that the 11-lncRNA signature could serve as an independent prognostic indicator. This study also suggests that these 11 lncRNAs potentially participate in the progression of OVC.

Identification of an Immune-Related Nine-lncRNA Signature Predictive of Overall Survival in Colon Cancer

Growing evidence suggests that immune-related genes (IRGs) and long non-coding RNAs (lncRNAs) can serve as prognostic markers of overall survival (OS) in patients with colon cancer. This study aimed to identify an immune-related lncRNA signature for the prospective assessment of prognosis in these patients. Gene expression and clinical data of colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Immune-related lncRNAs were identified by a correlation analysis between IRGs and lncRNAs. In total, 447 samples were divided into a training cohort (224 samples) and a testing cohort (223 samples). Univariate, lasso and multivariate Cox regression analyses identified an immune-related nine-lncRNA signature closely related to OS in colon cancer patients in the training dataset. A risk score formula involving nine immune-related lncRNAs was developed to evaluate the prognostic value of the lncRNA signature in the training dataset. Colon cancer patients with a high risk score had poorer OS than those with a low risk score. A multivariate Cox regression analysis confirmed that the immune-related nine-lncRNA signature could be an independent prognostic factor in colon cancer patients. The results were further confirmed in the testing cohort and the entire TCGA cohort. Furthermore, a gene set enrichment analysis revealed several pathways with significant enrichment in the high- and low-risk groups that may be helpful in formulating clinical strategies and understanding the underlying mechanisms. Finally, a quantitative real-time polymerase chain reaction assay found that the nine lncRNAs were significantly differentially expressed in colon cancer cell lines. The results of this study indicate that this signature has important clinical implications for improving predictive outcomes and guiding individualized treatment in colon cancer patients. These lncRNAs could be potential biomarkers affecting the prognosis of colon cancer.

The predictive value of lncRNA MIR31HG expression on clinical outcomes in patients with solid malignant tumors

Background

Emerging studies have explored the prognostic value of MIR31HG in cancers, but its role remains elusive. Herein, we aimed to summarize the prognostic potential of MIR31HG in this study.

Methods

Several databases were searched for literature retrieval on Dec 5, 2019. Overall and subgroup analyses were conducted to measure the relationship between MIR31HG expression and clinical outcomes. Moreover, GEPIA was applied for validation of prognostic value of MIR31HG in tumor patients in TCGA dataset.

Results

Overall, seventeen studies with 2573 patients were enrolled. Compared to counterparts, those patients with high MIR31HG expression tended to have shorter RFS. Notably, MIR31HG overexpression predicted unfavorable OS in lung cancer. By contrast, gastrointestinal cancer patients with elevated MIR31HG expression predicted better OS and disease-free survival. Additionally, MIR31HG overexpression was significantly associated with worse clinicopathological features including advanced tumor stage and LNM in lung cancer, but favorable clinical characteristics in gastrointestinal cancer. Moreover, the positive association between MIR31HG and OS in lung cancer was further confirmed in TCGA dataset.

Conclusion

Overexpression of MIR31HG suggested remarkable association with poor prognosis in terms of OS, tumor stage, and LNM in lung cancer, but favorable prognosis in gastrointestinal cancer. Therefore, MIR31HG may serve as a promising prognostic biomarker in multiple cancers.

Long Non-coding Wilms Tumor 1 Antisense RNA in the Development and Progression of Malignant Tumors

A growing number of studies have shown that long non-coding RNAs (lncRNAs) play an important role in tumor development and progression and are key molecules affecting tumor progression. The lncRNA Wilms tumor 1 antisense RNA (WT1-AS) is specifically expressed in various malignant tumors. In particular, WT1-AS expression is upregulated in colon cancer and breast cancer but is significantly downregulated in cervical cancer, liver cancer, and kidney cancer. The level of WT1-AS expression is closely related to the size, stage, and patient survival rate of these cancers. In this article, we review the modes of action, expression, function, and mechanisms of WT1-AS in different tumors to provide new targets for tumor diagnosis and treatment.

Two precision medicine predictive tools for six malignant solid tumors: from gene-based research to clinical application

Background

The current study aimed to construct competing endogenous RNA (ceRNA) regulation network and develop two precision medicine predictive tools for colorectal cancer (CRC).

Methods

Differentially expressed (DE) analyses were performed between CRC tissues and normal tissues. A ceRNA regulation network was constructed based on DElncRNAs, DEmiRNAs, and DEmRNAs.

Results

Fifteen mRNAs (ENDOU, MFN2, FASLG, SHOC2, VEGFA, ZFPM2, HOXC6, KLK10, DDIT4, LPGAT1, BEX4, DENND5B, PHF20L1, HSP90B1, and PSPC1) were identified as prognostic biomarkers for CRC by multivariate Cox regression. Then a Fifteen-mRNA signature was developed to predict overall survival for CRC patients. Concordance indexes were 0.817, 0.838, and 0.825 for 1-, 2- and 3-year overall survival. Patients with high risk scores have worse OS compared with patients with low risk scores.

Conclusion

The current study provided deeper understanding of prognosis-related ceRNA regulatory network for CRC. Two precision medicine predictive tools named Smart Cancer Survival Predictive System and Gene Survival Analysis Screen System were constructed for CRC. These two precision medicine predictive tools can provide valuable precious individual mortality risk prediction before surgery and improve the individualized treatment decision-making.

Comprehensive Analysis of a Competing Endogenous RNA Network Identifies Seven-lncRNA Signature as a Prognostic Biomarker for Melanoma

Long non-coding RNAs (LncRNAs) can act as competing endogenous RNA (ceRNA) involving in tumor initiation and progression. Nevertheless, the prognostic roles of lncRNAs in lncRNA-related ceRNA network of melanoma remain elusive. In this study, RNA sequence profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and there were 2020 differentially expressed messenger RNAs (DEmRNAs), 438 differentially expressed lncRNAs (DElncRNAs) and 65 differentially expressed microRNAs (DEmiRNAs) between primary and metastasis melanoma patients. A ceRNA regulatory network was constructed based on the DElncRNAs-DEmiRNAs and DEmiRNAs-DEmRNAs interactions, which contained 39 lncRNAs, 10 miRNAs, and 16 mRNAs. Furthermore, univariate and multivariate Cox regression analysis were carried out to establish a 7-lncRNA prognostic signature. Subsequently, the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve and the Kaplan-Meier risk survival analysis revealed the significant performance of this signature. Finally, pathway enrichment analyses implied that lncRNA MIR205HG and MIAT were associated with multiple cancer-related pathways, especially epidermis development and immune response. The current study provides novel insights into the lncRNA-related ceRNA network and the potential of lncRNAs to be candidate prognostic biomarkers and therapeutic targets in melanoma.

Cancer-related long noncoding RNAs show aberrant expression profiles and competing endogenous RNA potential in esophageal adenocarcinoma

Long non-coding RNAs (lncRNAs) govern gene expression by competitively binding to microRNA response elements (MREs). Although they were initially considered as transcriptional noise, lncRNAs have attracted increased attention in oncology. Dysregulation of lncRNAs occurs in various types of human tumor, including esophageal adenocarcinoma (EAC). However, the functions of these cancer-associated lncRNAs and of their related competitive endogenous RNA (ceRNA) network in EAC remains unknown. To determine the relevant potential mechanisms, the present study analyzed the transcriptome sequencing data and clinical information of 79 patients with EAC, including 79 tumor samples and 11 normal samples, which were obtained from The Cancer Genome Atlas esophageal cancer project. The edgeR v3.25.0 software was used for differential gene expression analysis. The results exhibited 561 cancer-associated lncRNAs with a >2.0-fold change and a false discovery rate-adjusted P<0.01. Among these lncRNAs, 26 were significantly associated with patient overall survival. According to data from bioinformatics databases and differentially expressed RNAs, an lncRNA-regulated ceRNA network for EAC was constructed. The results demonstrated that the aberrantly expressed lncRNA-associated ceRNA network included 37 EAC cancer-associated lncRNAs, five miRNAs and 13 mRNAs. In conclusion, the present study identified novel lncRNAs as candidate prognostic biomarkers and revealed a potential regulatory network of gene expression in EAC.

Comprehensive analysis of five long noncoding RNAs expression as competing endogenous RNAs in regulating hepatoma carcinoma

Liver cancer is the most common cancer and is the epitome of a recalcitrant cancer. Increasing evidence shown that long noncoding RNAs (lncRNA) were associated with cancer‐related death and could function as a competing endogenous RNA (ceRNA). To explore regulatory roles and potential prognostic biomarkers of lncRNA for liver cancer, RNA‐sequencing expression data were downloaded from the TCGA database and GEO database. A total of 357 patients were randomly divided into a discovery group and a validation group, of which 313 patients can obtain clinical data. In discovery phrase, 58 lncRNAs, 16 miRNAs, and 34 mRNAs were screened to construct the ceRNA network based on 252 patients employed from discovery group. Univariate and multivariate Cox hazard regression analysis model revealed that five lncRNAs (AATK‐AS1, C10orf91, LINC00162, LINC00200, and LINC00501) from 58 lncRNAs were formulated to predict the overall survival (OS). We used the value of gene expression and regression coefficients to construct a risk score based on the five lncRNAs. Next, we validated our model in the GSE116174 dataset (n = 64) and the validation group (n = 94) from TCGA database. Subgroup analysis suggest that the five lncRNAs played critical parts in early stage in cancer from both discovery and validation groups. The five lncRNAs were also found to be associated with immune cells infiltration including CD4⁺ memory activated, NK cells activated and mast cells activated, then the results were also validated according to the validation group. Furthermore, KEGG pathway enrichment analysis showed that these nine coexpressed modules using the method of WGCNA, and many of these pathways are associated with the development and progression of disease. At last, the transcription factor binding sites (TFBS) of the five lncRNAs were predicted, which help us to understand the potential mechanism that the TFBS adjusted the ceRNA network. In summary, the ceRNA regulatory network may contribute to a better understanding of liver cancer mechanism and provide potential prognostic biomarkers and therapeutic targets.

Prognostic lncRNAs, miRNAs, and mRNAs Form a Competing Endogenous RNA Network in Colon Cancer

Purpose: To develop a multi-RNA-based model to provide survival risk prediction for colon cancer by constructing a competing endogenous RNAs (ceRNAs) network.

Methods: The prognostic information and expression of the lncRNAs, miRNAs, and mRNAs in colon cancer specimens from The Cancer Genome Atlas (TCGA) were assessed. Constructing prognostic models used the differentially expressed RNAs. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and Gene Ontology were used to identify the functional role of the ceRNA network in the prognosis of colon cancer.

Results: Five lncRNAs (AC007384.1, AC002511.1, AC012640.1, C17orf82, and AP001619.1), 8 miRNAs (hsa-mir-141, hsa-mir-150, hsa-mir-375, hsa-mir-96, hsa-mir-107, hsa-mir-106a, hsa-mir-200a, and hsa-mir-1271), and 5 mRNAs (BDNF, KLF4, SESN2, SMOC1, and TRIB3) were highly correlated with tumor status and tumor stage. Three prognostic models based on the 5 lncRNAs, 8 miRNAs, and 5 mRNAs were constructed. The prognostic ability was 0.850 for the lncRNA-based model, 0.811 for the miRNA-based model, and 0.770 for the mRNA-based model. Patients with high-risk scores revealed worse overall survival. The KEGG pathways were significantly enriched in the “neuroactive ligand-receptor interaction.”

Conclusion: This study identified several potential prognostic biomarkers to construct a multi-RNA-based prognostic model for colon cancer.

The Role of Extracellular Vesicles as Modulators of the Tumor Microenvironment, Metastasis and Drug Resistance in Colorectal Cancer

Colorectal cancer (CRC) is one of the most common cancers worldwide, with high morbidity and mortality rates. A number of factors including modulation of the tumor microenvironment, high metastatic capability, and resistance to treatment have been associated with CRC disease progression. Recent studies have documented that tumor-derived extracellular vesicles (EVs) play a significant role in intercellular communication in CRC via transfer of cargo lipids, proteins, DNA and RNAs to the recipient tumor cells. This transfer influences a number of immune-related pathways leading to activation/differentiation/expression of immune cells and modulation of the tumor microenvironment that plays a significant role in CRC progression, metastasis, and drug resistance. Furthermore, tumor-derived EVs are secreted in large amounts in biological fluids of CRC patients and as such the expression analysis of EV cargoes have been associated with prognosis or response to therapy and may be a source of therapeutic targets. This review aims to provide a comprehensive insight into the role of EVs in the modulation of the tumor microenvironment and its effects on CRC progression, metastasis, and drug resistance. On the other hand, the potential role of CRC derived EVs as a source of biomarkers of response and therapeutic targets will be discussed in detail to understand the dynamic role of EVs in CRC diagnosis, treatment, and management.

Long noncoding RNAs predict the survival of patients with colorectal cancer as revealed by constructing an endogenous RNA network using bioinformation analysis

Long noncoding RNAs (lncRNAs) are aberrantly expressed in various cancers types and can function as competing endogenous RNAs (ceRNAs), which promote and maintain tumor initiation and progression. In this study, we explored the functional roles and regulatory mechanisms of lncRNAs as ceRNAs in colorectal cancer and their clinical potential as biomarkers. The RNA sequencing profiles of patients with colorectal cancer were downloaded from TCGA database, and 62 lncRNAs, 30miRNAs, and 59 mRNAs were identified to comprise the ceRNA network (fold change > 2, P < 0.01). Functional enrichment analysis suggested that the target genes of the ceRNA network may be involved in the pathways related to cancer, including the signaling pathway that regulates the pluripotency of stem cells, wnt signaling pathway, hippo signaling pathway, basal cell carcinoma, and colorectal cancer. Univariate and multivariate Cox's proportional hazard regression model revealed that five (H19, MIR31HG, HOTAIR, WT1‐AS, and LINC00488) out of 62 lncRNAs were closely related to the overall survival (OS) (P < 0.05). Furthermore, the five‐lncRNA model could be an independent prognostic model in colorectal cancer. We computed for the risk function and constructed a risk score based on the five lncRNAs. Results showed that patients with high‐risk scores have poor survival rates. Additionally, combing the risk score and other clinicopathological features, we can better predict the patient's survival probabilities. Furthermore, we validate our model in the GSE38832 dataset. Collectively, our study has provided a deeper understanding of the lncRNA‐related ceRNA regulatory mechanism in CRC and identified five‐lncRNA model, which could be considered as candidate prognostic biomarkers and therapeutic targets.