A long non-coding RNA expression signature to predict survival of patients with colon adenocarcinoma

Long non-coding RNAs - SNHG6 emerge as potential marker in colorectal cancer

Colorectal cancer (CRC) ranks among the leading cancers in terms of incidence and mortality in the Western world. Currently, there are no sufficient diagnostic markers that would enable an early diagnosis and efficient therapy. Unfortunately, a significant number of new CRC cases is detected in late stages, with distant metastases, therefore, new therapeutic approaches, which would alleviate the prognosis for advanced stages of CRC, are highly in demand. SNHG6 belongs to the group of long non-coding RNAs, which are a larger entity of RNAs consisting of >200 nucleotides. SNHG6 is expressed mainly in the cell cytoplasm, where it acts as a regulator of numerous processes: modulation of crucial protein hubs; sponging miRNAs and upregulating the expression of their target mRNAs; and interacting with various cellular pathways including TGF-β/Smad and Wnt/β-catenin. SNHG6 is an oncogene, substantially overexpressed in CRC tissues and cancerous cell lines as compared to healthy samples. Its overexpression is associated with higher grade, lymphovascular invasion and tumor size. Taking into consideration the role of SNHG6 in the colorectal tumorigenesis, invasion and metastasis, we summarized its role in CRC and conclude that it could serve as a potential biomarker in CRC diagnosis and prognosis assessment.

A Three-Genes Signature Predicting Colorectal Cancer Relapse Reveals LEMD1 Promoting CRC Cells Migration by RhoA/ROCK1 Signaling Pathway

Objective

Colorectal cancer (CRC) patients that experience early relapse consistently exhibit poor survival. However, no effective approach has been developed for the diagnosis and prognosis prediction of postoperative relapsed CRC.

Methods

Multiple datasets from the GEO database and TCGA database were utilized for bioinformatics analysis. WGCNA analyses and RRA analysis were performed to identify key genes. The COX/Lasso regression model was used to construct the recurrence model. Subsequent in vitro experiments further validated the potential role of the hub genes in CRC.

Results

A comprehensive analysis was performed on multiple CRC datasets and a CRC recurrence model was constructed containing LEMD1, SERPINE1, and SIAE. After further validation in two independent databases, we selected LEMD1 for in vitro experiments and found that LEMD1 could regulate CRC cell proliferation, migration, invasion, and promote EMT transition. The Rho-GTPase pulldown experiments further indicated that LEMD1 could affect RhoA activity and regulate cytoskeletal dynamics. Finally, we demonstrated that LEMD1 promoted CRC cell migration through the RhoA/ROCK1 signaling pathway.

Conclusions

In this study, a CRC relapse model consisting of LEMD1, SERPINE1, and SIAE was constructed by comprehensive analysis of multiple CRC datasets. LEMD1 could promote CRC cell migration through the RhoA/ROCK signaling pathway.

Emerging Roles and Mechanisms of lncRNA FOXD3-AS1 in Human Diseases

Numerous long noncoding RNAs (lncRNAs) have been identified as powerful regulators of human diseases. The lncRNA FOXD3-AS1 is a novel lncRNA that was recently shown to exert imperative roles in the initialization and progression of several diseases. Emerging studies have shown aberrant expression of FOXD3-AS1 and close correlation with pathophysiological traits of numerous diseases, particularly cancers. More importantly, FOXD3-AS1 was also found to ubiquitously impact a range of biological functions. This study aims to summarize the expression, associated clinicopathological features, major functions and molecular mechanisms of FOXD3-AS1 in human diseases and to explore its possible clinical applications.

VPS9D1-AS1, a novel long-non-coding RNA, acts as a tumor promoter by regulating the miR-324-5p/ITGA2 axis in colon adenocarcinoma

BACKGROUND

Colon adenocarcinoma (COAD) is among the most common malignancies worldwide. Elucidating the function and mechanism of action of the lncRNA VPS9D1-AS1 in COAD will be of great value for identifying potential therapeutic targets.

METHODS

Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was used to measure the expression levels of lncRNA VPS9D1-AS1 in COAD tissues and cell lines. After knocking down the expression of VPS9D1-AS1 in two COAD cell lines, namely SW1116 and LoVo, their proliferation rate was measured by the 5-ethynyl-2'-deoxyuridine (Edu) incorporation and cell counting kit-8 (CCK-8) viability assays, migration and invasion abilities were assessed by wound healing and Transwell assays, and apoptosis rate was measured withflow cytometry. Additionally, the dual luciferase reporter assay system was used to investigate the targeting of miR-324-5p to VPS9D1-AS1 and ITGA2 3'-UTR. The inhibitory effects of the miR-324-5p/ITGA2 axis on the function of VPS9D1-AS1 were also examined. In vivo tumorigenesis assay was performed in nude mice injected with VPS9D1-AS1 shRNA or control shRNA lentivirus-transfected LoVo cells.

RESULTS

VPS9D1-AS1 was found to be upregulated in COAD tissues and cell lines. VPS9D1-AS1 knockdown inhibited the COAD cell proliferation, migration and invasion and increased the apoptosis rate. In addition, we have demonstrated that miR-324-5p targets VPS9D1-AS1 and ITGA2 3'-UTR, and miR-324-5p silencing or forced ITGA2 expression attenuated the effect of VPS9D1-AS1 knockdown.

CONCLUSION

This study identified a novel competing endogenous RNA (ceRNA) pathway that potentially associates with the oncogenic functions of VPS9D1-AS1, miR-324-5p, and ITGA2 in COAD, which could contribute to the identification of new therapeutic approaches targeting COAD.

KDM5D inhibits the transcriptional activation of FKBP4 by suppressing the expression of E2F1 in colorectal cancer in males

Colorectal cancer (CRC) remains the most frequently diagnosed malignancy and also a major contributor to cancer-related death throughout the world. Here, we first revealed the role of histone lysine-specific demethylase 5D (KDM5D) in CRC in males. KDM5D expression in tumor and adjacent tissues of male CRC patients was investigated using immunohistochemistry and RT-qPCR, and the correlation between its expression and patients' prognosis was analyzed. Downregulation of KDM5D in CRC patients was associated with poor prognoses. Overexpression of KDM5D significantly inhibited the growth and metastasis of CRC in vitro and in vivo. The downstream mechanism of KDM5D in CRC was investigated using bioinformatics analysis, and the regulatory relationship was confirmed by ChIP-qPCR and luciferase reporter assays. KDM5D suppressed E2F1 expression by mediating H3K4me3 demethylation. E2F1, highly expressed in CRC, promoted the expression of FKBP4 at the transcriptional level by binding to the FKBP4 promoter. Finally, rescue experiments revealed that overexpression of FKBP4 significantly reversed the inhibitory effect of KDM5D on CRC growth and metastasis. Collectively, KDM5D exerted an anti-tumor and anti-metastatic in CRC through demethylation in E2F1 and suppression of FKBP4 transcription, which might represent a novel target in CRC treatment in male.

MicroRNA-708 targeting ZNF549 regulates colon adenocarcinoma development through PI3K/AKt pathway

Colon adenocarcinoma (COAD) is the most common type of gastrointestinal cancer and is still the third leading cause of cancer-related mortality worldwide. Therefore, finding new and promising drugs to eradicate cancer may be a feasible method to treat COAD patients. Cys2-His2 zinc finger proteins (ZFPs) is one of the largest transcription factor family and many of them are highly involved in regulation of cell differentiation, proliferation, apoptosis, and neoplastic transformation. In this study, we identified a tumor-inhibiting factor, ZNF549, which expressed lowly in COAD tissues and COAD cell lines (HT29, HCT116, SW480, LoVo, and SW620). Overexpression of ZNF549 inhibit the ability of COAD cell proliferation and migration. On the contrary, decreasing the ZNF549 expression level promote the ability of COAD cell proliferation and migration. Through bioinformatics analysis, we found that ZNF549 was a potential target of hsa-miR-708-5p (miR-708-5p). Furthermore, we verified the possibility of miR-708-5p targeting the ZNF549 gene, and miR-708-5p inhibited the expression of ZNF549 by luciferase reporter assays, qRT-PCR and western blot assays. Moreover, the relationship between miR-708-5p and phosphatidylinositol 3-kinase/AKt (PI3K/AKt) signal pathway was elucidated. Overexpression and inhibition of miR-708-5p resulted in increased and decreased expression of p-AKt and p-PI3K in HCT116 cells, respectively. RT-qPCR and western blot assays results demonstrated that miR-708-5p regulated COAD cells development by promoting the process of Epithelial-mesenchymal transition (EMT) through PI3K/AKt signaling pathway. In summary, our findings demonstrated that ZNF549, the target gene of miR-708-5p, functions as a tumor suppressor to inhibit COAD cell lines proliferation and migration through regulate the PI3K/AKt signal pathway.

Microarray profiling of differentially expressed lncRNAs and mRNAs in lung adenocarcinomas and bioinformatics analysis

Long noncoding RNAs (lncRNAs) dysregulation leads to malignant progression of lung cancer. Our study profiled differentially expressed lncRNA and mRNA in tumor and normal tissues of lung adenocarcinoma (LUAD). Further, analysis of the gene expression profiles of LUAD tissues (n = 533) and normal tissues (n = 59) from The Cancer Genome Atlas (TCGA). A total of 138 lncRNAs were differentially expressed between LUAD tissues and normal tissues (false discovery rate [FDR] q < 0.05, fold change (FC) ≥ 2), a number of which are key regulators of multiple cancer and biological processes in humans. For example, lncRNA A2M‐AS1 displayed the highest correlation with the co‐expressed mRNAs, indicating that it might play a key role in regulating differential gene expression in LUAD. The data from the current study of the comprehensive lncRNA expression profile in LUAD tissues provided useful information to guide the identification of potential LUAD biomarkers.

Development and validation of a five-lncRNA signature with prognostic value in colon cancer

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients' overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.

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.

A 6 lncRNA-Based Risk Score System for Predicting the Recurrence of Colon Adenocarcinoma Patients

Colon adenocarcinoma (COAD) is a common type of colon cancer, and post-operative recurrence and metastasis may occur in COAD patients. This study is designed to build a risk score system for COAD patients. The Cancer Genome Atlas (TCGA) dataset of COAD (the training set) was downloaded, and GSE17538 and GSE39582 (the validation sets) from Gene Expression Omnibus database were obtained. The differentially expressed RNAs (DERs) were analyzed by limma package. Using survival package, the independent prognosis-associated long non-coding RNAs (lncRNAs) were selected for constructing risk score system. After the independent clinical prognostic factors were screened out using survival package, a nomogram survival model was constructed using rms package. Furthermore, competitive endogenous RNA (ceRNA) regulatory network and enrichment analyses separately were performed using Cytoscape software and DAVID tool. Totally 404 DERs between recurrence and non-recurrence groups were identified. Based on the six independent prognosis-associated lncRNAs (including H19, KCNJ2-AS1, LINC00899, LINC01503, PRKAG2-AS1, and SRRM2-AS1), the risk score system was constructed. After the independent clinical prognostic factors (Pathologic M, pathologic T, and RS model status) were identified, the nomogram survival model was built. In the ceRNA regulatory network, there were three lncRNAs, four miRNAs, and 77 mRNAs. Additionally, PPAR signaling pathway and hedgehog signaling pathway were enriched for the mRNAs in the ceRNA regulatory network. The risk score system and the nomogram survival model might be used for predicting COAD recurrence. Besides, PPAR signaling pathway and hedgehog signaling pathway might affect the recurrence of COAD patients.

A stroma-related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early-stage colon cancer

The heterogeneity in prognoses and chemotherapeutic responses of colon cancer patients with similar clinical features emphasized the necessity for new biomarkers that help to improve the survival prediction and tailor therapies more rationally and precisely. In the present study, we established a stroma‐related lncRNA signature (SLS) based on 52 lncRNAs to comprehensively predict clinical outcome. The SLS model could not only distinguish patients with different recurrence and mortality risks through univariate analysis, but also served as an independent factor for relapse‐free and overall survival. Compared with the conventionally used TNM stage system, the SLS model clearly possessed higher predictive accuracy. Moreover, the SLS model also effectively screened chemotherapy‐responsive patients, as only patients in the low‐SLS group could benefit from adjuvant chemotherapy. The following cell infiltration and competing endogenous RNA (ceRNA) network functional analyses further confirmed the association between the SLS model and stromal activation‐related biological processes. Additionally, this study also identified three phenotypically distinct colon cancer subtypes that varied in clinical outcome and chemotherapy benefits. In conclusion, our SLS model may be a significant determinant of survival and chemotherapeutic decision‐making in colon cancer and may have a strong clinical transformation value.

Prediction of overall survival time in patients with colon adenocarcinoma using DNA methylation profiling of long non-coding RNAs

Long non-coding RNAs (lncRNAs) are a subgroup of RNAs able to regulate gene expression at the epigenetic level, and are therefore central to the regulation of numerous biological processes and the progression of multiple cancer types. However, lncRNAs have not been identified to considerably influence overall survival (OS) outcome in numerous different types of cancer. The majority of studies investigating the association between lncRNAs and epigenetic regulation have focused on their altered expression levels in cancerous cells, and few studies have focused on determining the correlation between lncRNAs and OS time. In the present study, comprehensive lncRNA expression analysis was performed on a cohort of patients diagnosed with colon adenocarcinoma (COAD) using the least absolute shrinkage and selection operator method (LASSO). Subsequently, the construction of a prognostic methylation-based predictive system was performed based on the results of LASSO analysis. Functional enrichment analysis of lncRNA co-expression genes was also performed. According to the results of the present study, the classifier was able to significantly predict the prognosis of patients with COAD, and the investigation of the relevant elucidated genes further revealed the mechanism of COAD pathogenesis.

Identification of key pathways and genes in colorectal cancer to predict the prognosis based on mRNA interaction network

The aim of the present study was to identify key genes in colorectal cancer (CRC) that could be used to reliably diagnose this disease and to explore the potential underlying mechanisms in silico. The gene expression profiles of primary human cancer datasets GSE21510 and GSE32323 were downloaded from the Gene Expression Omnibus database. The limma R software package was used to identify differentially expressed (DE) genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on DE genes using the Database for Annotation, Visualization and Integrated Discovery. The Search Tool for the Retrieval of Interacting Genes/Proteins database was used to construct a protein-protein interaction (PPI) network of the DE genes. Survival rate was analyzed and visualized using The Cancer Genome Atlas (TCGA). A total of 1,126 genes were significantly DE in the present study. All DE genes were enriched in KEGG pathways including 'cell cycle', 'mineral absorption', 'pancreatic secretion', 'pathways in cancer', 'metabolic pathways', 'aldosterone-regulated sodium reabsorption' and 'Wnt signaling pathway'. A total of 5 hub genes enriched in cell cycle and tumor-associated pathways, including E2F2, SKP2, MYC, CDKN1A and CDKN2B, were significantly DE and validated between tumor and normal tissues. CDKN1A and CDKN2B were identified within the PPI network using the Molecular Complex Detection algorithm. Survival and content distribution analyses of 362 clinical samples from TCGA revealed that CDKN1A effectively predicted the prognosis of patients. The present study identified key genes and potential signaling pathways involved in CRC. These findings may provide new insights for survival assessment during the clinical diagnosis of CRC.

Integrated bioinformatics analysis revealing independent prognostic long non-coding RNAs DNAH17-AS1 and RP11-400N13.2 and their potential oncogenic roles in colorectal cancer

The aberrant expression of long non-coding RNAs (lncRNAs) has been associated with a variety of malignancies, including colorectal cancer (CRC); however, the key lncRNAs associated with patient prognosis and their biological roles in CRC are yet to be determined. The aim of the present study was to determine the key lncRNAs associated with patient prognosis as well as their biological roles in CRC. Therefore, a dataset from The Cancer Genome Atlas containing the lncRNA expression data of 521 CRC and normal colorectal mucosal tissues, as well as the corresponding clinical data, were screened. A total of 1,180 significantly differentially expressed lncRNAs were associated with CRC as determined by t-tests in edgeR. Kaplan-Meier analysis revealed that 56 of the 1,180 lncRNAs were associated with overall survival (OS); 7 of the 56 lncRNAs were identified as key lncRNAs associated with the Tumor-Node-Metastasis stage of CRC by Kruskal-Wallis test. Subsequent univariate and multivariate Cox regression analyses of the 7 lncRNAs revealed 2 lncRNAs, DNAH17-AS1 and RP11-400N13.2, as potential independent prognostic factors for the OS of patients with CRC. Furthermore, the expression levelsof these 2 lncRNAs were significantly upregulated in CRC compared with those in normal tissues, which suggested that they may serve an oncogenic role in CRC. In addition, networks comprising the 2 lncRNAs and their respective co-expressed protein-coding genes (PCGs) were constructed using cor.test in R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of these PCGs were conducted; DNAH17-AS1- and RP11-400N13.2-associated PCGs were reported to be involved in G-protein coupling-related functions. Thus, these independent prognostic lncRNAs and their associated functions identified in the present study may provide novel insight into potential prognostic biomarkers and therapeutic targets for the treatment of CRC.

Upregulation of long non-coding RNA SNHG6 promote esophageal squamous cell carcinoma cell malignancy and its diagnostic value

AIM

To investigate the expression of lncRNA SNHG6 in esophageal squamous cell carcinoma (ESCC), the biological function of SNHG6 in ESCCs, and evaluate its diagnostic value in ESCC.

METHODS

SNHG6 expression in tissues and cells was detected by quantitative real-time polymerase chain reaction (qRT-PCR). The effect of SNHG6 on the cell proliferation, migration and invasion was detected by CCK-8 assay and transwell assay, respectively.

RESULTS

SNHG6 expression was upregulated in ESCC tissues and ESCCs. Upregulated SNHG6 expression was correlated with prognosis, lymph node metastasis, distant metastasis and TNM stage of ESCC patients. SNHG6 promoted the abilities of cell proliferation, migration and invasion in ESCCs, knockdown of SNHG6 reversed these effects. SNHG6 might serve as an independently diagnostic biomarker for lymph node metastasis, distant metastasis and TNM stage.

CONCLUSION

SNHG6 may exert oncogenic function in ESCC and may be a potential diagnostic marker for this cancer.

Development of somatic mutation signatures for risk stratification and prognosis in lung and colorectal adenocarcinomas

Background

Prognostic signatures are vital to precision medicine. However, development of somatic mutation prognostic signatures for cancers remains a challenge. In this study we developed a novel method for discovering somatic mutation based prognostic signatures.

Results

Somatic mutation and clinical data for lung adenocarcinoma (LUAD) and colorectal adenocarcinoma (COAD) from The Cancer Genome Atlas (TCGA) were randomly divided into training (n = 328 for LUAD and 286 for COAD) and validation (n = 167 for LUAD and 141 for COAD) datasets. A novel method of using the log2 ratio of the tumor mutation frequency to the paired normal mutation frequency is computed for each patient and missense mutation. The missense mutation ratios were mean aggregated into gene-level somatic mutation profiles. The somatic mutations were assessed using univariate Cox analysis on the LUAD and COAD training sets separately. Stepwise multivariate Cox analysis resulted in a final gene prognostic signature for LUAD and COAD. Performance was compared to gene prognostic signatures generated using the same pipeline but with different somatic mutation profile representations based on tumor mutation frequency, binary calls, and gene-gene network normalization. Signature high-risk LUAD and COAD cases had worse overall survival compared to the signature low-risk cases in the validation set (log-rank test p-value = 0.0101 for LUAD and 0.0314 for COAD) using mutation tumor frequency ratio (MFR) profiles, while all other methods, including gene-gene network normalization, have statistically insignificant stratification (log-rank test p-value ≥0.05). Most of the genes in the final gene signatures using MFR profiles are cancer-related based on network and literature analysis.

Conclusions

We demonstrated the robustness of MFR profiles and its potential to be a powerful prognostic tool in cancer. The results are robust according to validation testing and the selected genes are biologically relevant.

Development and internal validation of a nine-lncRNA prognostic signature for prediction of overall survival in colorectal cancer patients

Background

Colorectal cancer remains a serious public health problem due to the poor prognosis. In the present study, we attempted to develop and validate a prognostic signature to predict the individual mortality risk in colorectal cancer patients.

Materials and Methods

The original study datasets were downloaded from The Cancer Genome Atlas database. The present study finally included 424 colorectal cancer patients with wholly gene expression information and overall survival information.

Results

A nine-lncRNA prognostic signature was built through univariate and multivariate Cox proportional regression model. Time-dependent receiver operating characteristic curves in model cohort demonstrated that the Harrell's concordance indexes of nine-lncRNA prognostic signature were 0.768 (95% CI [0.717-0.819]), 0.778 (95% CI [0.727-0.829]) and 0.870 (95% CI [0.819-0.921]) for 1-year, 3-year and 5-year overall survival respectively. In validation cohort, the Harrell's concordance indexes of nine-lncRNA prognostic signature were 0.761 (95% CI [0.710-0.812]), 0.801 (95% CI [0.750-0.852]) and 0.883 (95% CI [0.832-0.934]) for 1-year, 3-year and 5-year overall survival respectively. According to the median of nine-lncRNA prognostic signature score in model cohort, 424 CRC patients could be stratified into high risk group (n = 212) and low risk group (n = 212). Kaplan-Meier survival curves showed that the overall survival rate of high risk group was significantly lower than that of low risk group (P < 0.001).

Discussion

The present study developed and validated a nine-lncRNA prognostic signature for individual mortality risk assessment in colorectal cancer patients. This nine-lncRNA prognostic signature is helpful to evaluate the individual mortality risk and to improve the decision making of individualized treatments in colorectal cancer patients.

Long noncoding RNA SNHG6 promotes the progression of colorectal cancer through sponging miR-760 and activation of FOXC1

Background

Colorectal cancer (CRC) is one of most common cancers worldwide. Long non-coding RNA SNHG6 has been reported to act as essential regulators in several cancers. However, the functional role and molecular mechanism of SNHG6 in colorectal cancer remain unclear.

Methods

Quantitative real-time polymerase chain reaction (PCR) was performed to evaluate the SNHG6 expression in CRC tissues. Colony formation, transwell assays and in vivo mice models were carried out to assess the effect of SNHG6 on CRC biological functions.

Results

In the present study, we showed that the expression of SNHG6 was significantly upregulated in CRC tissues and cell lines. High expression of SNHG6 was associated with shorter overall survival in CRC patients. Functionally, SNHG6 knockdown significantly inhibited cell proliferation, invasion and migration both in vitro and in vivo. Mechanically, miR-760 was a direct target of SNHG6, and repression of miR-760 could rescue the inhibitory effect of SNHG6 knockdown on CRC progression. In addition, SNHG6 positively regulated FOXC1 expression through sponging miR-760 in CRC cells, thus indicating that SNHG6 exerted an oncogenic role in CRC by acting as a ceRNA of miR-760.

Conclusion

Our results indicate that long non-coding RNA SNHG6 promotes colorectal cancer progression by sequestering miR-760 and activating FOXC1, our findings suggest that SNHG6 may serve as a potential therapeutic target for CRC.

Recurrence-Associated Long Non-coding RNA Signature for Determining the Risk of Recurrence in Patients with Colon Cancer

Patients with colon cancer are often faced a high risk of disease recurrence within 5 years of treatment that is the major cause of cancer mortality. Reliable molecular markers were required to improve the most effective personalized therapy. Here, we identified a recurrence-associated six-lncRNA (long non-coding RNA) signature (LINC0184, AC105243.1, LOC101928168, ILF3-AS1, MIR31HG, and AC006329.1) that can effectively distinguish between high and low risk of cancer recurrence from 389 patients of a discovery dataset, and validated its robust performance in four independent datasets comprising a total of 906 colon cancer patients. We found that the six-lncRNA signature was an independent predictive factor of disease recurrence in multivariate analysis and was superior to the performance of clinical factors and known gene signature. Furthermore, in silico functional analysis showed that the six-lncRNA-signature-associated coding genes are significantly enriched in proliferation and angiogenesis, cell death, as well as critical cancer pathways that could play important roles in colon cancer recurrence. Together, the six-lncRNA signature holds great potential for recurrence risk assessment and personalized management of colon cancer patients.