Long noncoding RNA LINC01234 promoted cell proliferation and invasion via miR-1284/TRAF6 axis in colorectal cancer

Colorectal cancer is one of the most common and leading malignancies globally. Long noncoding RNAs (lncRNAs) function as potentially critical regulator in colorectal cancer. LINC01234, a novel lncRNA in tumor biology, regulates the progression of various tumors. However, the tumorigenic mechanism of LINC01234 in colorectal cancer is still unclear. This study was performed with the aim to prospectively investigate clinical significance, effect, and mechanism of lncRNA LINC01234 in colorectal cancer. First, we found that LINC01234, localized in the cytoplasm, was increased in both colorectal cancer cell lines and tissues. Subsequent functional assays suggested LINC01234 knockdown suppressed cell proliferation, migration, and invasion of colorectal cancer cells, while blocked cell cycle and induced cell apoptosis. Moreover, we identified that miR-1284 was target of LINC01234, we further demonstrated a negative correlation with LINC01234 in colorectal cancer tissues and cells. Furthermore, miR-1284 targeted and suppressed tumor necrosis factor receptor–associated factor 6 (TRAF6). Loss-of-function assay revealed that LINC01234 silencing suppressed colorectal cancer progression through inhibition of miR-1284. In vivo subcutaneous xenotransplanted tumor model indicated LINC01234 knockdown inhibited in vivo tumorigenic ability of colorectal cancer via downregulation of TRAF6. Collectively, this study clarified the biological significance of LINC01234/miR-1284/TRAF6 axis in colorectal cancer progression, providing insights into LINC01234 as novel potential therapeutic target for colorectal cancer therapeutic from bench to clinic.     

Long noncoding RNA MIR155HG facilitates pancreatic cancer progression through negative regulation of miR-802

Long noncoding RNAs (lncRNAs) present the key regulatory functions in tumorigenesis. More and more studies have suggested that lncRNA MIR155HG is involved in different human cancers. However, the underlying regulatory role of lncRNA MIR155HG and potential mechanisms in pancreatic cancer (PC) remain illusive. In this research, our group found that lncRNA MIR155HG expression was remarkably increased in PC tumor tissue and cells compared to that in the adjacent normal tissue and cells. In addition, higher MIR155HG expression was positively associated with the poor prognosis of patients. In addition, we exhibited that silence of MIR155HG by short hairpin RNA knockdown significantly inhibited cell growth and promoted cell apoptosis in PC cells. We performed bioinformatics analysis to search for the target of MIR155HG. As demonstrated by Luciferase reporter assay, we found that miR-802, a tumor suppressor in various cancer, is a direct target of MIR155HG. We demonstrated that the tumor-promoting effects of MIR155HG were contributed by negative regulation of miR-802 in PC cells. In summary, our results suggest that lncRNA MIR155HG might be applied as a novel diagnostic and therapeutic target for PC.     

Integrated analysis of co‐expression and ceRNA network identifies five lncRNAs as prognostic markers for breast cancer

Long non‐coding RNAs (lncRNAs), which competitively bind miRNAs to regulate target mRNA expression in the competing endogenous RNAs (ceRNAs) network, have attracted increasing attention in breast cancer research. We aim to find more effective therapeutic targets and prognostic markers for breast cancer. LncRNA, mRNA and miRNA expression profiles of breast cancer were downloaded from TCGA database. We screened the top 5000 lncRNAs, top 5000 mRNAs and all miRNAs to perform weighted gene co‐expression network analysis. The correlation between modules and clinical information of breast cancer was identified by Pearson's correlation coefficient. Based on the most relevant modules, we constructed a ceRNA network of breast cancer. Additionally, the standard Kaplan‐Meier univariate curve analysis was adopted to identify the prognosis of lncRNAs. Ultimately, a total of 23 and 5 modules were generated in the lncRNAs/mRNAs and miRNAs co‐expression network, respectively. According to the Green module of lncRNAs/mRNAs and Blue module of miRNAs, our constructed ceRNA network consisted of 52 lncRNAs, 17miRNAs and 79 mRNAs. Through survival analysis, 5 lncRNAs (AL117190.1, COL4A2‐AS1, LINC00184, MEG3 and MIR22HG) were identified as crucial prognostic factors for patients with breast cancer. Taken together, we have identified five novel lncRNAs related to prognosis of breast cancer. Our study has contributed to the deeper understanding of the molecular mechanism of breast cancer and provided novel insights into the use of breast cancer drugs and prognosis.     

Five key lncRNAs considered as prognostic targets for predicting pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and the 5‐year survival rate was only 7.7%. To improve prognosis, a screening biomarker for early diagnosis of pancreatic cancer is in urgent need. Long non‐coding RNA (lncRNA) expression profiles as potential cancer prognostic biomarkers play critical roles in development of tumorigenesis and metastasis of cancer. However, lncRNA signatures in predicting the survival of a patient with PDAC remain unknown. In the current study, we try to identify potential lncRNA biomarkers and their prognostic values in PDAC. LncRNAs expression profiles and corresponding clinical information for 182 cases with PDAC were acquired from The Cancer Genome Atlas (TCGA). A total of 14 470 lncRNA were identified in the cohort, and 175 PDAC patients had clinical variables. We obtained 108 differential expressed lncRNA via R packages. Univariate and multivariate Cox proportional hazards regression, lasso regression was performed to screen the potential prognostic lncRNA. Five lncRNAs have been recognized to significantly correlate with OS. We established a linear prognostic model of five lncRNA (C9orf139, MIR600HG, RP5‐965G21.4, RP11‐436K8.1, and CTC‐327F10.4) and divided patients into high‐ and low‐risk group according to the prognostic index. The five lncRNAs played independent prognostic biomarkers of OS of PDAC patients and the AUC of the ROC curve for the five lncRNAs signatures prediction 5‐year survival was 0.742. In addition, targeted genes of MIR600HG, C9orf139, and CTC‐327F10.4 were explored and functional enrichment was also conducted. These results suggested that this five‐lncRNAs signature could act as potential prognostic biomarkers in the prediction of PDAC patient's survival.     

Aberrant expression of lncRNAs SNHG6, TRPM2-AS1, MIR4435-2HG,and hypomethylation of TRPM2-AS1 promoter in colorectal cancer

Accumulating evidence has indicated that deregulation of lncRNAs plays essential roles in colorectal cancer (CRC) carcinogenesis. The goal of this study was to analyze the expression of lncRNAs in colorectal cancer and their association with clinicopathological variables. Bioinformatics analysis of published CRC microarray data was performed to identify the important lncRNAs. The expression levels of candidate genes were assessed in the human colon cancer/normal cell lines, CRC, adenomatous colorectal polyps, and their marginal tissues by qRT-PCR. Moreover, the methylation status of the TRPM2-AS1 promoter was studied using qMSP assay. Furthermore, we investigated the molecular mechanisms of these lncRNAs in CRC progression using in silico analysis. Microarray analysis revealed that lncRNAs SNHG6, MIR4435-2HG, and TRPM2-AS1 were upregulated in CRC. These results were validated in colon cell lines. Moreover, qRT-PCR showed that the expression levels of SNHG6 and TRPM2-AS1 were upregulated in the colorectal tumor tissues compared with their paired tissues. Nonetheless, there was no significant increase in MIR4435-2HG expression in CRC samples. Furthermore, we observed a significant hypomethylation of TRPM2-AS1 promoter and its activation in CRC tissues. By in silico analysis, we found that the lncRNAs upregulation could promote proliferation and drug resistance of colorectal cancer cells via miRNAs sponging and modulation of their targets expression. In conclusion, based on our results upregulation of SNHG6 and TRPM2-AS1, and hypomethylation of TRPM2-AS1 promoter might be considered as potential diagnostic biomarkers for CRC initiation and development.     

LINC01133 inhibits breast cancer invasion and metastasis by negatively regulating SOX4 expression through EZH2

Mounting evidence highlights long non‐coding RNAs (lncRNAs) as crucial regulators in multiple types of biological processes and contributing to tumourigenesis. LINC01133, located in chromosome 1q23.2, was a recently identified novel lncRNA with a length of 1154nt. It was involved in the development of colorectal cancer and non‐small cell lung cancer. However, its clinical relevance, biological functions and potential molecular mechanism in breast cancer are still unclear. In this study, we found that the LINC01133 expression was significantly down‐regulated in breast cancer samples and was associated with progression and poor prognosis of breast cancer. Further experiments demonstrated that overexpression of LINC01133 inhibited invasion and metastasis in breast cancer both in vitro and in vivo. Mechanistic investigations revealed that LINC01133 repressed SOX4 expression by recruiting EZH2 to SOX4 promoter. Moreover, rescue experiments further confirmed that LINC01133 functional acted as an anti‐oncogene, at least partly, via repressing SOX4 in breast cancer. Taken together, these findings imply that LINC01133 could serve as a novel prognostic biomarker and potential therapeutic target for breast cancer.     

Establishment and validation of lncRNA-related prognostic signatures in cholangiocarcinoma

BackgroundThe prognosis of CCA is extremely poor, making it one of the most lethal cancers. Therefore, there is a need to elucidate the pathogenic mechanisms of CCA. In this study, we aimed at identifying lncRNA-related prognostic signatures for CCA through bioinformatics analysis and further validated their functions in CCA tumorigenesis and progression.MethodsThe RNA-seq data of CCA were downloaded from public databases. Differentially expressed lncRNAs (DElncRNAs) were screened. Then, candidate OS- and DFS-related DElncRNAs were selected through Kaplan–Meier survival analysis. Furthermore, LASSO regression was performed to establish the OS and DFS signatures, respectively. Multivariate COX models and nomograms for overall survival (OS) and disease-free survival (DFS) were established based on OS/DFS signature and clinical data. Hub lncRNAs were identified and enrichment analyses were performed to explore their potential functions. Finally, in vitro and in vivo models were used to validate the effects of the hub lncRNAs in CCA tumorigenesis and progression.ResultsA total of 925 DElncRNAs were selected, of which six candidate OS-related lncRNAs and 15 candidate DFS-related lncRNAs were identified. The OS and DFS signatures were then established using four lncRNAs, respectively. We found that the OS signature and vascular invasion were independent risk factors for the OS of CCA, while the DFS signature, vascular invasion, and CA19–9 were independent risk factors for the DFS of CCA. Then, nomograms were established to achieve personalized CCA recurrence and death prediction. Furthermore, our study uncovered that MIR4435-2HG and GAPLINC might play crucial roles in CCA progression and be selected as hub lncRNAs. GO and KEGG enrichment analyses revealed that the two hub lncRNAs were closely related to CCA tumorigenesis. Finally, we demonstrated that MIR4435-2HG and GAPLINC can stimulate CCA proliferation and migration in vitro and in vivo.ConclusionsThe established OS and DFS signatures are independent risk factors for OS and DFS of CCA patients, respectively. MIR4435-2HG and GAPLINC were identified as hub lncRNAs. In vitro and in vivo models revealed that MIR4435-2HG and GAPLINC can prompt CCA progression, which might be novel prognostic biomarkers and therapeutic targets for CCA.     

Genome-wide screening and cohorts validation identifying novel lncRNAs as prognostic biomarkers for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the main subtype of renal cell carcinoma with varied prognosis. We aimed to identify and assess the possible prognostic long noncoding RNA (lncRNA) biomarkers. LncRNAs expression data and corresponding clinical information of 619 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Differentially expressed genes analysis, univariate Cox regression, the least absolute shrinkage and selection operator Cox regression model were utilized to identify hub lncRNAs. Multivariate Cox regression was used to establish the risk model. Statistical analysis was performed using R 3.5.3. The expression value of five lncRNAs and the risk-score levels were significantly associated with a survival prognosis of ccRCC patients (all P < .001). In the TCGA validation cohort, the area under the curve (AUC) for the integrated nomogram was 0.905 and 0.91 for 3-, 5-year prediction separately. The AUC reached up to 0.757 in an independent ICGC cohort. Besides, the calibration plots also illustrated well curve-fitting between observation values and predictive values. Weighted gene co-expression network analysis and subsequent pathway analysis revealed that the PI3K-Akt-mTOR and hypoxia-inducible factor signaling crosstalk might function as the most essential mechanisms related to the five-lncRNAs signature. Our study suggested that lncRNA AC009654.1, AC092490.2, LINC00524, LINC01234, and LINC01885 were significantly associated with ccRCC prognosis. The prognostic model based on this five lncRNA may predict the overall survival of ccRCC.     

Downregulation of long noncoding RNA LINC00683 associated with unfavorable prognosis in prostate cancer based on TCGA

Prostate cancer (PCa) is the third most common reason of cancer-related deaths in men. Accumulating evidence has shown that dysregulation of long noncoding RNAs (lncRNAs) is closely related to cancer initiation and development. Although large numbers of lncRNAs have been discovered, knowledge regarding their function and physiological/pathological significance remains limited. In this study, we aimed to reveal functional lncRNAs and identify prognosis-related RNAs in PCa by analyzing data from The Cancer Genome Atlas (TCGA). To achieve this, an lncRNA-mRNA coexpression network was constructed by weighted correlation network analysis. Additionally, a subnetwork was extracted from this weighted correlation network, and seven lncRNAs were identified as core nodes. Further Kaplan-Meier survival analysis showed that three lncRNAs (LINC00683, LINC00857, and FENDRR) were significantly downregulated in PCa samples, and there was a strong positive correlation with patient survival. Importantly, LINC00683 has not been fully reported as related with PCa. Additionally, gene set enrichment analysis indicated that LINC00683 might be involved in cancer-related pathways such as the Wnt pathway. Based on the findings of this study, lncRNA LINC00683 is likely to provide a new diagnostic biomarker and therapeutic target for future PCa treatments.     

EA15, MIR22, LINC00472 as diagnostic markers for diabetic kidney disease

This study aimed to investigate the molecular mechanisms of diabetic kidney disease (DKD) and to explore new potential therapeutic strategies and biomarkers for DKD. First we analyzed the differentially expressed changes between patients with DKD and the control group using the chip data in Gene Expression Omnibus (GEO) database. Then the gene chip was subjected to be annotated again, so as to screen long noncoding RNAs (lncRNAs) and study expression differences of these lncRNAs in DKD and controlled samples. At last, the function of the differential lncRNAs was analyzed. A total of 252 lncRNAs were identified, and 14 were differentially expressed. In addition, there were 1,629 differentially expressed messenger RNAs (mRNAs) genes, and proliferation and apoptosis adapter protein 15 (PEA15), MIR22, and long intergenic nonprotein coding RNA 472 ( LINC00472) were significantly differentially expressed in DKD samples. Through functional analysis of the encoding genes coexpressed by the three lncRNAs, we found these genes were mainly enriched in type 1 diabetes and autoimmune thyroid disease pathways, whereas in Gene Ontology (GO) function classification, they were also mainly enriched in the immune response, type I interferon signaling pathways, interferon-γ mediated signaling pathways, and so forth. To summary, we identified EA15, MIR22, and LINC00472 may serve as the potential diagnostic markers of DKD.     

A novel autophagy-related lncRNA survival model for lung adenocarcinoma

Long non-coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy-related lncRNA in lung adenocarcinoma. In this study, autophagy-related mRNAs-lncRNAs were screened from lung adenocarcinoma and a co-expression network of autophagy-related mRNAs-lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy-related lncRNAs and finally obtained a survival model composed of 11 autophagy-related lncRNAs. Through Kaplan-Meier analysis, univariate and multivariate Cox regression analysis and time-dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low-risk and high-risk groups. These 11 lncRNAs were GAS6-AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA-DQB1-AS1, LINC01116, LINC01806, FAM83A-AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196-1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149-1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy-related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy-related therapeutic targets.