A Tumor-Specific Prognostic Long Non-Coding RNA Signature in Gastric Cancer

A pyroptosis-related lncRNA-based prognostic index for hepatocellular carcinoma by relative expression orderings

Background

Hepatocellular carcinoma (HCC) is an invasive malignant tumor, and pyroptosis makes an important contribution to the pathology and progression of liver cancer. Many prognostic models have been proposed for HCC based on the quantitative expression level of candidate genes, which are unsuitable for clinical application due to their vulnerability against experimental batch effects. The aim of this study was to develop a novel pyroptosis-related long non-coding RNA (lncRNA)-based prognostic index (PLPI) for HCC based on relative expression orderings (REOs).

Methods

Firstly, the pyroptosis-related lncRNAs were identified through the Wilcoxon rank-sum test and gene co-expression analyses. Then, the novel prognostic model PLPI was constructed by pyroptosis-related lncRNA pairs, which were identified by multiple machine learning algorithms. Gene set enrichment, somatic mutation, and drug sensitivity analyses were conducted to measure the differences between high- and low-risk patients. Multiple immune analyses were used to explore the association between PLPI and the immunological microenvironment.

Results

In this study, a novel prognostic model PLPI based on 10 pyroptosis-related lncRNA pairs was constructed, which was proven to be an independent prognostic risk factor. The receiver operating characteristic (ROC) curves showed that the model had a good prognostic ability in the training, testing, and external set, respectively [5-year area under the curve (AUC) =0.73, 5-year AUC =0.81, 4-year AUC =0.79]. The results of survival, somatic mutation, and immune analyses showed that the patients in the low-risk group had a better prognosis, lower rates of somatic mutation, and better immune cell infiltration. Personalized chemotherapeutic drugs were also identified for the patients with HCC.

Conclusions

The novel PLPI not only greatly predicted the prognosis of patients with HCC but could also offer novel ideas and approaches for the therapeutic management of HCC.

LncRNA SEMA3B-AS1 suppresses the tumor-initiating characteristics of triple negative breast cancer via engaging in MLL4-mediated H3K4 trimethylation

Long noncoding RNAs (lncRNAs) are crucial regulators of tumor-initiating cells (TICs) and hold particular importance in triple negative breast cancer (TNBC). Yet, the precise mechanisms by which TIC-associated lncRNAs influence TNBC remain unclear. Our research utilized The Cancer Genome Atlas Breast Cancer (BC) data set to identify prognostic lncRNAs. We then conducted extensive assays to explore their impact on the tumor-initiating phenotype of TNBC cells and the underlying mechanisms. Notably, we found that low expression of lncRNA SEMA3B-AS1 correlated with unfavorable survival in BC patients. SEMA3B-AS1 was also downregulated in TNBC and linked to advanced tumor stage. Functional experiments confirmed its role as a TIC-suppressing lncRNA, curtailing mammosphere formation, ALDH + TIC cell proportion, and impairing clonogenicity, migration, and invasion. Mechanistic insights unveiled SEMA3B-AS1's nuclear localization and interaction with MLL4 (mixed-lineage leukemia 4), triggering H3K4 methylation-associated transcript activation and thus elevating the expression of SEMA3B, a recognized tumor suppressor gene. Our findings emphasize SEMA3B-AS1's significance as a TNBC-suppressing lncRNA that modulates TIC behavior. This study advances our comprehension of lncRNA's role in TNBC progression, advocating for their potential as therapeutic targets in this aggressive BC subtype.

Coding and noncoding RNA profile of human heterotopic ossifications - Risk factors and biomarkers

Heterotopic ossification (HO) means the formation of bone in muscles and soft tissues, such as ligaments or tendons. HO could have a genetic history or develop after a traumatic event, as a result of muscle injury, fractures, burns, surgery, or neurological disorders. Many lines of evidence suggest that the formation of HO is related to the pathological differentiation of stem or progenitor cells present within soft tissues or mobilized from the bone marrow. The cells responsible for the initiation and progression of HO are generally called HO precursor cells. The exact mechanisms behind the development of HO are not fully understood. However, several factors have been identified as potential contributors. For example, local tissue injury and inflammation disturb soft tissue homeostasis. Inflammatory cells release growth factors and cytokines that promote osteogenic or chondrogenic differentiation of HO precursor cells. The bone morphogenetic protein (BMP) is one of the main factors involved in the development of HO. In this study, next-generation sequencing (NGS) and RT-qPCR were performed to analyze the differences in mRNA, miRNA, and lncRNA expression profiles between muscles, control bone samples, and HO samples coming from patients who underwent total hip replacement (THR). As a result, crucial changes in the level of gene expression between HO and healthy tissues were identified. The bioinformatic analysis allowed to describe the processes most severely impacted, as well as genes which level differed the most significantly between HO and control samples. Our analysis showed that the level of transcripts involved in leukocyte migration, differentiation, and activation, as well as markers of chronic inflammatory diseases, that is, miR-148, increased in HO, as compared to muscle. Furthermore, the levels of miR-195 and miR-143, which are involved in angiogenesis, were up-regulated in HO, as compared to bone. Thus, we suggested that inflammation and angiogenesis play an important role in HO formation. Importantly, we noticed that HO is characterized by a higher level of TLR3 expression, compared to muscle and bone. Thus, we suggest that infection may also be a risk factor in HO development. Furthermore, an increased level of transcripts coding proteins involved in osteogenesis and signaling pathways, such as ALPL, SP7, BGLAP, BMP8A, BMP8B, SMPD3 was noticed in HO, as compared to muscles. Interestingly, miR-99b, miR-146, miR-204, and LINC00320 were up-regulated in HO, comparing to muscles and bone. Therefore, we suggested that these molecules could be important biomarkers of HO formation and a potential target for therapies.

Clinicopathological and prognostic value of lncRNAs expression in gastric cancer: A field synopsis of observational studies and databases validation

BACKGROUND

The purpose of this study was to evaluate existing evidence in the field of long non-coding RNAs (lncRNAs) and prognosis of gastric cancer.

METHODS

A comprehensive literature search was performed through the electronic database. The combined hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) of overall survival (OS), disease-free survival (DFS), or progression free survival (PFS) were calculated to assess the strength of the association. Kaplan-Meier (KM) plotter was used to verify lncRNA HOX transcript antisense RNA (HOTAIR) expression and OS.

RESULTS

Overall, a significant correlation between high lncRNAs expression and poor OS was explored in patients with gastric cancer (HR = 1.78, P < .001). Subgroup analysis based on statistical methods indicated the high expression of lncRNAs in log-rank (HR = 1.87, P < .001) and multivariate analysis (HR = 1.71, P < .001) were all significantly correlated with the poor OS. Clinicopathological parameters analysis showed the lncRNA expression were significantly associated prognosis, including TNM stage, tumor size, pathological differentiation, lymph nodes metastasis, distance metastasis, invasion depth and Lauren's classification. It was consistent with the verification results of bioinformatics database for lncRNA HOTAIR (P < .001).

CONCLUSION

Our study confirmed the expression of lncRNAs and clinicopathological features may serve as effective indicators of prognosis in patients with gastric cancer.

Construction of prognostic signature of patients with oral squamous cell carcinoma based on pyroptosis-related long non-coding RNAs

Background and objective

Oral squamous cell carcinoma (OSCC) is the most common malignant tumor in the head and neck, and its morbidity and mortality are increasing year by year. Changes in key genes are thought to be closely related to the occurrence and development of OSCC. Pyroptosis is an inflammatory form of programmed cell death that has been implicated in malignancies and inflammatory diseases. Changes in the expression of long noncoding RNAs may also affect tumorigenesis and progression. In this study, our main objective was to evaluate the association between pyroptosis-related lncRNAs and prognosis in patients with OSCC.

Methods

The RNA-seq data and clinicopathological data of OSCC patients are from The Cancer Genome Atlas database. The pyroptosis gene set is obtained from Gene Set Enrichment Analysis database. Univariate COX, Lasso and multivariate COX regression analyses were used for the construction of risk prognostic models of OSCC, eight lncRNAs were incorporated into prognostic models. The Kaplan-Meier method and log-rank test were used to evaluate the differences of overall survival between patients in high-risk and low-risk groups. The reliability of predictions across the dataset was analyzed by receiver operating characteristic (ROC) curves. The immune signature score was calculated using the single-sample gene set enrichment analysis.

Results

Eight pyroptosis-related lncRNAs were used to construct prognostic signature of OSCC, including AC136475.2, AC024075.2, JPX, ZFAS1, TNFRSF10A-AS1, LINC00847, AC099850.3 and IER3-AS1. According to this prognostic signature, patients with OSCC were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis showed that the survival rate of the high-risk group was significantly lower than the low-risk group. ROC area for risk score was 0.716, and ROC area of the 8 lncRNAs are all between 0.5 and 1, implied that these lncRNAs had high accuracy in predicting the prognosis of OSCC patients. Immune Infiltration findings suggested that these lncRNAs affected immune responses in the microenvironment of OSCC.

Conclusion

The prognostic signature based on pyroptosis-related lncRNAs potentially serves as an independent prognostic indicator for OSCC patients. And this signature facilitates research on targeted diagnosis and treatment of patients diagnosed with OSCC.

Inconsistency in the expression pattern of a five-lncRNA signature as a potential diagnostic biomarker for gastric cancer patients in bioinformatics and in vitro

Objectives

Due to diagnosis of gastric cancer in advanced stages as well as its poor prognosis, finding biomarkers is essential. In this study, using the TCGA RNAseq data of gastric cancer patients, we evaluated the diagnostic value of lncRNAs that had differential expression.

Materials and Methods

We evaluated P-value, FDR, and log fold change for whole transcripts. Next, by comparison of the RNAseq gene names with the total known lncRNA names, we identified differential expressed lncRNAs. Following this, specificity and sensitivity for lncRNAs coming from the previous step were calculated. For more confirmation, we predicted target genes and performed GO and KEGG signaling pathway analysis. In the end, we examined reliability and consistency of expression of this signature in three gastric cancer cell lines and one of them in twenty tumors and tumor-adjacent normal tissue samples using qRT-PCR.

Results

Five lncRNAs had proper sensitivity and specificity and had target genes involved in cancer-related signaling pathways; however, they showed different expression patterns in TCGA data and in vitro.

Conclusion

The results of our study demonstrated that the five-lncRNAs PART1, UCA1, DIRC3, HOTAIR, and HOXA11AS require more investigation to be confirmed as diagnostic biomarkers in gastric cancer.

Differential Expression of Super-Enhancer-Associated Long Non-coding RNAs in Uterine Leiomyomas

Super-enhancer-associated long non-coding RNAs (SE-lncRNAs) are a specific set of lncRNAs transcribed from super-enhancer (SE) genomic regions. Recent studies have revealed that SE-lncRNAs play essential roles in tumorigenesis through the regulation of oncogenes. The objective of this study was to elucidate the expression profile of SE-lncRNAs with concurrent assessment of associated mRNAs in leiomyomas and paired myometrium. Arraystar SE-lncRNAs arrays were used to systematically profile the differentially expressed SE-lncRNAs along with the corresponding SE-regulated protein coding genes in eight leiomyomas and paired myometrium. The analysis indicated 7680 SE-lncRNAs were expressed, of which 721 SE-lncRNAs were overexpressed, while 247 SE-lncRNAs were underexpressed by 1.5-fold or greater in leiomyoma. Thirteen novel SE-lncRNAs and their corresponding protein coding genes were selected, and their expression was confirmed in eighty-one paired leiomyoma tissues by quantitative real-time PCR. The thirteen pairs of SE-lncRNAs and their corresponding protein coding genes included RP11-353N14.2/CBX4, SOCS2-AS1/SOCS2, RP1-170O19.14/HOXA11, CASC15/PRL, EGFLAM-AS1/EGFLAM, RP11-225H22/NEURL1, RP5-1086K13.1/CD58, AC092839.3/SPTBN1, RP11-69I8.3/CTGF, TM4SF1-AS1/TM4SF1, RP11-373D23/FOSL2, RP11-399K21.11/COMTD1, and CTB-113P19.1/SPARC. Among these SE-lncRNAs, the expression of SOCS2-AS1/SOCS2, RP11-353N14.2/CBX4, RP1-170O19.14/HOXA11, and RP11-225H22/NEURL1 was significantly higher in African Americans as compared with Caucasians. The expression of RP11-353N14.2/CBX4, SOCS2-AS1/SOCS2, CASC15/PRL, and CTB-113P19.1/SPARC was significantly higher in tumors with MED12-mutation-positive as compared with MED12-mutation-negative tumors. Collectively, our results indicate that the differential expression of SE in leiomyomas is another mechanism contributing to dysregulation of protein coding genes in leiomyomas and that race and MED12 mutation can influence the expression of a select group of SE.

Long noncoding RNA TNFRSF10A-AS1 promotes colorectal cancer through upregulation of HuR

BACKGROUND

Recent studies have emphasized the emerging importance of long noncoding RNAs (lncRNAs) in colorectal cancer (CRC). However, the functions and regulatory mechanisms of numerous lncRNAs in CRC have not been fully elucidated.

AIM

To explore the functional role and underlying molecular mechanisms of lncRNA TNFRSF10A-AS1 in CRC.

METHODS

TNFRSF10A-AS1 expression was measured by quantitative real-time polymerase chain reaction in CRC, and the relationship between TNFRSF10A-AS1 levels and the clinicopathological features of CRC patients was analyzed. The effect of TNFRSF10A-AS1 expression on CRC proliferation and metastasis was examined in vitro and in vivo. Mechanistically, we investigated how TNFRSF10A-AS1 is involved in CRC as a competitive endogenous RNA.

RESULTS

TNFRSF10A-AS1 was expressed at a high level in CRC and the upregulation of TNFRSF10A-AS1 was associated with advanced T grade and tumor size in CRC patients. A functional investigation revealed that TNFRSF10A-AS1 enhanced the proliferation, migration ability and invasion ability of colon cancer cells in vitro and in vivo. A mechanistic analysis demonstrated that TNFRSF10A-AS1 acted as a miR-3121-3p molecular sponge to regulate HuR expression, ultimately promoting colorectal tumorigenesis and progression.

CONCLUSION

TNFRSF10A-AS1 exerts a tumor-promoting function through the miR-3121-3p/HuR axis in CRC, indicating that it may be a novel target for CRC therapy.

Comparative clinical studies of primary chemoradiotherapy versus S-1 and nedaplatin chemotherapy against stage IVb oesophageal squamous cell carcinoma: a multicentre open-label randomised controlled trial

INTRODUCTION

Oesophageal squamous cell carcinoma (OSCC) is one of the most commonly occurring devastating tumours worldwide, including in China. To date, the standard care of patients with stage IV OSCC is systemic chemotherapy and palliative care, which results in poor prognosis. However, no consensus has been established regarding the role of radiotherapy in targeting the primary tumour in patients with stage IVa OSCC. Thus, the aim of this study is to assess the effectiveness of primary radiotherapy combined with S-1 and nedaplatin (NPD) chemotherapy in the patients with stage IV OSCC.

METHODS AND ANALYSIS

The study is a multicentre, open-label, randomised controlled trial. A total of 180 eligible patients with stage IV OSCC will be randomised into a study group (90 patients) and a control group (90 patients). Patients in the study group will receive radiotherapy to the primary tumour at a dose of 50.4 Gy combined with 4-6 cycles of S-1 and NPD chemotherapy. In the control group, patients will only receive 4-6 cycles of S-1 and NPD chemotherapy. The primary and secondary outcomes will be measured. The differences between the two groups will be statistically analysed with regard to overall survival, the progression-free survival and safety. All outcomes will be ascertained before treatment, after treatment and after the follow-up period.The results of this study will provide evidence on the role of radiotherapy in patients with stage IV OSCC in China, which will show new options for patients with advanced oesophageal cancer.

ETHICS AND DISSEMINATION

This study was approved by the Institutional Ethics Committee of The First Hospital Affiliated of Zhengzhou University (approval number: SS-2018-04).

TRIAL REGISTRATION

The trial has been registered at the Chinese Clinical Trial Registry (ChiCTR1800015765) on 1 November 2018; retrospectively registered, http://www.chictr.org.cn/index.aspx.

Testing lncRNAs signature as clinical stage–related prognostic markers in gastric cancer progression using TCGA database

LncRNA expression can be conducive to gastric cancer (GC) prognosis. The objective of this study is to ascertain five specific lncRNAs involved in tumor progression of GC and their role as prognostic markers to diagnose clinical stage-wise GC. High-throughput RNA sequencing data were obtained from The Cancer Genome Atlas (TCGA) database and performed genome-wide lncRNA expression analysis using edgeR package, Bioconductor.org , and R-statistical computing to analyze differentially expressed lncRNA analysis. Cutoff parameters were FDR < 0.05 and |Log2FC| > 2. Total 351 tumor samples with differentially expressed lncRNAs were divided into group-1 lncRNAs such as AC019117.2 and LINC00941, and group-2 lncRNAs such as LINC02410, AC012317.2, and AC141273.1 by 2:1. The Spearman correlation coefficients ( p < 0.05) and correlation test function (cor.test ()) were performed for lncRNAs as per clinical stage. Cytoscape software was used to construct lncRNA–mRNA interaction networks. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway ( p < 0.05) analysis were conducted using the clusterProfiler package. Kaplan–Meier survival analysis was performed to determine the overall survival of patients based on the expression of five lncRNAs in different clinical stages of GC. AC019117.2 and LINC00941 of group 1 inferred a positive correlation with clinical stages of stage I to stage IV, and their expressions were higher in tumor tissues than normal tissues. On the contrary, LINC02410, AC012317.2, and AC141273.1 of group 2 exhibited a negative correlation with clinical stage, and they exhibited more expression in normal tissues compared to tumor tissues. GO and KEGG pathway analysis reported that AC019117.2 may interact with LINC00941 via ITGA3 and trophoblast glycoprotein (TPBG) to foster tumor progression. Tumor-specific group-1 lncRNAs were conducive to the poor overall survival and exhibited a positive correlation with the clinical stages of stage I to stage IV in GC as per the lncRNA–mRNA networking analysis. These five lncRNAs could be considered as clinically useful lncRNA-based prognostic markers to predict clinical stage-wise GC progression.

Immune-Related Gene Expression Analysis Revealed Three lncRNAs as Prognostic Factors for Colon Cancer

Colorectal cancer (CRC) is one of the most common cancers. Almost 80% of CRC cases are colon adenocarcinomas (COADs). Several studies have indicated the role of immunotherapy in the treatment of various cancers. Our study aimed to identify immune-related long non-coding RNAs (lncRNAs) and to use them to construct a risk assessment model for evaluating COAD prognosis. Using differential expression, correlation, and Cox regression analyses, we identified three immune-related differentially expressed lncRNAs (IR-DELs) and used them to construct a risk assessment model. The area under the curve (AUC) for each receiver operating characteristic (ROC) curve at 3-, 5-, and 10-years were greater than 0.6. In addition, the risk assessment model was correlated with several immune cells and factors. The three IR-DELs (AC124067.4, LINC02604, and MIR4435-2HG) identified in this study can be used to predict outcomes for patients with COAD and might help in identifying those who can benefit from anti-tumor immunotherapy.

The association of lncRNA SNPs and SNPs-environment interactions based on GWAS with HBV-related HCC risk and progression

Background

Long non‐coding RNA (lncRNA) plays an essential role in hepatitis B virus‐related hepatocellular carcinoma (HBV‐related HCC) occurrence and development. Single nucleotide polymorphism (SNP) may affect HBV‐related HCC susceptibility by altering the function of lncRNA. However, the relationship between lncRNA SNPs and HBV‐related HCC occurrence and development is still unclear.

Methods

In the present study, based on HBV‐related HCC genome‐wide association studies, eight potentially functional SNPs from two lncRNAs were predicted using a set of bioinformatics strategies. In 643 HBV‐related HCC patients, 549 CHB carriers, and 553 HBV natural clearance subjects from Southern Chinese, we evaluated associations between SNPs and HBV‐related HCC occurrence or development with odds ratio (OR) and 95% confidence interval (CI) under credible genetic models.

Results

In HBV‐related HCC patients, rs9908998 was found to significantly increase the risk of lymphatic metastasis under recessive model (Adjusted OR = 1.95, 95% CI = 1.20–3.17). Lnc‐RP11‐150O12.3 rs2275959, rs1008547, and rs11776545 with cancer family history may show significant multiplicative and additive interactions on HBV‐related HCC susceptibility (all p_Adjusted < .05). The associations of rs2275959, rs1008547, and rs11776545 with distant metastasis of HBV‐related HCC patients were observed in additive model (Adjusted OR = 1.45, 95% CI = 1.06–1.97 for rs2275959; Adjusted OR = 1.45, 95% CI = 1.06–1.98 for rs1008547; Adjusted OR = 1.40, 95% CI = 1.03–1.91 for rs11776545).

Conclusion

Taken together, lnc‐ACACA‐1 rs9908998, lnc‐RP11‐150O12.3 rs2275959, rs1008547, and rs11776545 might be predictors for HBV‐related HCC risk or prognosis.

Identification of Long Noncoding RNA Biomarkers for Hepatocellular Carcinoma Using Single-Sample Networks

Objective

Many studies have found that long noncoding RNAs (lncRNAs) are differentially expressed in hepatocellular carcinoma (HCC) and closely associated with the occurrence and prognosis of HCC. Since patients with HCC are usually diagnosed in late stages, more effective biomarkers for early diagnosis and prognostic prediction are in urgent need.

Methods

The RNA-seq data of liver hepatocellular carcinoma (LIHC) were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs and mRNAs were obtained using the edgeR package. The single-sample networks of the 371 tumor samples were constructed to identify the candidate lncRNA biomarkers. Univariate Cox regression analysis was performed to further select the potential lncRNA biomarkers. By multivariate Cox regression analysis, a 3-lncRNA-based risk score model was established on the training set. Then, the survival prediction ability of the 3-lncRNA-based risk score model was evaluated on the testing set and the entire set. Function enrichment analyses were performed using Metascape.

Results

Three lncRNAs (RP11-150O12.3, RP11-187E13.1, and RP13-143G15.4) were identified as the potential lncRNA biomarkers for LIHC. The 3-lncRNA-based risk model had a good survival prediction ability for the patients with LIHC. Multivariate Cox regression analysis proved that the 3-lncRNA-based risk score was an independent predictor for the survival prediction of patients with LIHC. Function enrichment analysis indicated that the three lncRNAs may be associated with LIHC via their involvement in many known cancer-associated biological functions.

Conclusion

This study could provide novel insights to identify lncRNA biomarkers for LIHC at a molecular network level.

Long non‑coding RNA RP11‑400N13.3 promotes the progression of colorectal cancer by regulating the miR‑4722‑3p/P2RY8 axis

Accumulating evidence has shown that long non-coding RNAs (lncRNAs) play significant roles in the development and progression of many types of cancer including colorectal cancer. RP11-400N13.3 is a novel lncRNA discovered recently and its biological function and underlying mechanism in colorectal cancer remain elusive. This study aimed to reveal the relationship between RP11-400N13.3 and colorectal cancer. Our results demonstrated that the expression of RP11-400N13.3 was significantly upregulated in both colorectal cancer tissues and cell lines as compared to normal adjacent tissues and normal colonic epithelial cells by RT-qPCR, respectively. Upregulation of RP11-400N13.3 was found to be correlated with a poor overall survival rate. Functional studies revealed that RP11-400N13.3 facilitated the proliferation, migration, invasion and tumor growth of colorectal cancer cells while inhibiting the apoptosis of cancer cells in vitro and in vivo. We also observed that RP11-400N13.3 serves as a sponge for miR-4722-3p, and that P2Y receptor family member 8 (P2RY8) was predicted to be a target of miR-4722-3p by bioinformatics analysis. Western blot assay indicated that the expression of P2RY8 was negatively or positively regulated by miR-4722-3p or RP11-400N13.3. In addition, rescue experiments revealed that RP11-400N13.3 promoted proliferation, migration and invasion by directly regulating the expression of miR-4722-3p and P2RY8. In conclusion, our results revealed that RP11-400N13.3 promoted colorectal cancer progression via modulating the miR-4722-3p/P2RY8 axis, thus suggesting RP11-400N13.3 as a potential therapeutic target for the treatment of colorectal cancer.

Discovery of a novel three-long non-coding RNA signature for predicting the prognosis of patients with gastric cancer

Background

Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) play a predictive role in the prognosis of gastric cancer (GC). The present study aims to construct a lncRNA-based model via mining data of The Cancer Genome Atlas (TCGA).

Methods

Differentially expressed lncRNAs were first identified, followed by univariate Cox analysis, Robust likelihood-based survival model and multivariate Cox analysis to construct a signature composed of lncRNAs.

Results

A three-lncRNA based predictive signature (OVAAL, FLJ16779, FAM230D) was established to stratify GC patients into high- and low-risk groups. Patients in the high-risk group had markedly shorter overall survival (OS) than those in the low-risk group, which was verified by the ROC curve. Then, we validated the predictive power of the scoring system in other two cohorts. Multivariate Cox analysis also indicated that the 3-lncRNA signature was an independent prognostic factor for survival prediction in GC patients. Moreover, Gene Set Enrichment Analysis (GSEA) revealed that diverse metabolic pathways significantly clustered in the low-risk group, which might explain how the 3-lncRNA signature promoted gastric carcinogenesis.

Conclusions

We established a robust three-lncRNA model to predict the OS of GC patients, which might benefit the clinical decision making for personalized treatment and prognostic prediction for GC patients.

Comprehensive Analysis of Differentially Expressed lncRNAs in Gastric Cancer

Gastric cancer (GC) is the fourth most common malignant tumor. The mechanism underlying GC occurrence and development remains unclear. Previous studies have indicated that long non-coding RNAs (lncRNAs) are significantly associated with gastric cancer, but a systematic understanding of the role of lncRNAs in gastric cancer is lacking. In recent years, with the development of next-generation sequencing technology, tens of thousands of lncRNAs have been discovered. However, a large number of unannotated lncRNAs remain unidentified in different tissues, including potential gastric cancer-related lncRNAs. In this study, RNA sequencing (RNA-seq) data from 16 samples of eight gastric cancer patients were obtained and analyzed. A total of 1,854 previously unannotated lncRNAs were identified by ab initio assembly, and 520 differentially expressed lncRNAs were validated in the TCGA expression dataset. Methylation and copy number variation (CNV) array data from the same sample were integrated in the analysis. Changes in DNA methylation levels and CNVs may be responsible for the differential expression of 91 lncRNAs. Differentially expressed lncRNAs were enriched in coexpressed clusters of genes related to functions such as cell signaling, cell cycle, immune response, metabolic processes, angiogenesis, and regulation of retinoic acid (RA) receptors. Finally, a differentially expressed lncRNA, AC004510.3, was identified as a potential biomarker for the prediction of the overall survival of gastric cancer patients.

Long Noncoding RNA DLGAP1-AS1 Promotes the Aggressive Behavior of Gastric Cancer by Acting as a ceRNA for microRNA-628-5p and Raising Astrocyte Elevated Gene 1 Expression

Purpose

The long noncoding RNA DLGAP1 antisense RNA 1 (DLGAP1-AS1) plays well-defined roles in the malignant progression of hepatocellular carcinoma. The purpose of this study was to determine whether DLGAP1-AS1 affects the aggressive behavior of gastric cancer (GC).

Methods

DLGAP1-AS1 expression in GC tissue samples and cell lines was determined by reverse-transcription quantitative PCR. GC cell proliferation, apoptosis, migration, invasion, and tumor growth in vitro as well as in vivo were examined by the Cell Counting Kit-8 assay, flow-cytometric analysis, transwell migration and invasion assays, and xenograft model experiments, respectively.

Results

DLGAP1-AS1 was overexpressed in GC tissue samples and cell lines. Among patients with GC, the increased level of DLGAP1-AS1 correlated with tumor size, TNM stage, lymph node metastasis, distant metastasis, and shorter overall survival. The knockdown of DLGAP1-AS1 suppressed GC cell proliferation, migration, and invasion in vitro, as well as promoted cell apoptosis and hindered tumor growth in vivo. Mechanistically, DLGAP1-AS1 functioned as a competing endogenous RNA for microRNA-628-5p (miR-628-5p) in GC cells, thereby increasing the expression of the miR-628-5p target astrocyte elevated gene 1 (AEG-1). Functionally, the recovery of the miR-628-5p/AEG-1 axis output attenuated the effects of DLGAP1-AS1 knockdown in GC cells.

Conclusion

DLGAP1-AS1 is a pleiotropic oncogenic lncRNA in GC. DLGAP1-AS1 plays a pivotal part in the oncogenicity of GC in vitro and in vivo by regulating the miR-628-5p/AEG-1 axis. DLGAP1-AS1, miR-628-5p, and AEG-1 form a regulatory pathway to facilitate GC progression, suggesting this pathway as an effective target for the treatment of GC.

Prediction of gastric cancer prognosis in the next-generation sequencing era

Gastric cancer (GC) is one of the most commonly diagnosed malignancies worldwide, and is caused by complex interactions of multiple risk factors such as environmental (Helicobacter pylori and Epstein–Barr Virus), hereditary (genetic alterations and epigenetic modifications), as well as dietary and lifestyle factors. GC is usually detected at an advanced stage, with a dismal prognosis. Even for patients with similar clinical or pathologic stage receiving similar treatment, the outcomes are still uneven and unpredictable. To better incorporate genetic and epigenetic profiles into GC prognostic predication, gene expression signatures have been developed to predict GC outcomes. More recently, the advancement of high-throughput sequencing technology, also known as next-generation sequencing (NGS) technology, and analysis has provided the basis for accurate molecular classification of GC tumors. Here, we summarized and updated the literature related to NGS studies of GC, including whole-genome sequencing, whole-exome sequencing, RNA sequencing, and targeted sequencing, and discussed current progresses. NGS has facilitated the identification of genetic/epigenetic targets for screening as well as development of targeted agent therapy, thus enabling individualized patient management and treatment.

Development and validation of a prognostic signature for preoperative prediction of overall survival in gastric cancer patients

Background

As a serious challenge for public health, the prognosis of gastric cancer patients is still poor. The current study aimed to develop and validate a prognostic signature to predict the overall survival of gastric cancer patients.

Patients and methods

The dataset in the present study was obtained from The Cancer Genome Atlas database. The present study finally included 343 gastric cancer patients with information on long non-coding RNA (lncRNA) expression and overall survival.

Results

A prognostic model named Eleven-lncRNA signature was constructed according to the expression values of eleven prognostic lncRNA predictors identified by univariate and multivariate Cox regression model. According to time-dependent receiver operating characteristic curves, the Harrell's concordance indexes of Eleven-lncRNA signature were 0.764 (95% CI 0.720-0.808), 0.776 (95% CI 0.732-0.820), and 0.807 (95% CI 0.763-0.851) for 1-year overall survival, 3-year overall survival, and 5-year overall survival respectively in the model group. In the validation group, the Harrell's concordance indexes of Eleven-lncRNA signature were 0.748 (95% CI 0.704-0.792), 0.794 (95% CI 0.750-0.838), and 0.798 (95% CI 0.754-0.842) for 1-year overall survival, 3-year overall survival, and 5-year overall survival respectively. The gastric cancer patients (n=343) in the model group could be stratified into low-risk group (n=171) and high-risk group (n=172) according to the median of Eleven-lncRNA signature score. Kaplan-Meier survival curves showed that the mortality rate in the high-risk group was significantly poorer than that in the low-risk group (P<0.001).

Conclusion

The present study constructed and validated a prognostic model named Eleven-lncRNA signature for preoperative individual mortality risk prediction in gastric cancer patients. This Eleven-lncRNA signature can predict the individual mortality risk of gastric cancer patients and is helpful in improving clinical decision making regarding individualized treatment.

Long non‑coding RNA MLK7‑AS1 promotes proliferation in human colorectal cancer via downregulation of p21 expression

Current studies have highlighted long non‑coding RNAs (lncRNAs) as critical regulators in various cancers, including colorectal cancer (CRC). By utilizing publicly available data from The Cancer Genome Atlas dataset, MLK7 antisense RNA 1 (MLK7‑AS1) was identified as a novel lncRNA that correlated with CRC progression. The results of reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) revealed a significant upregulation of MLK7‑AS1 in both CRC tissue samples and cell lines. In addition, a positive correlation was observed between increased MLK7‑AS1 expression and several clinicopathological factors in patients with CRC. Importantly, MLK7‑AS1 knockdown suppressed CRC cell proliferation and promoted G1/G0 phase arrest and apoptosis in vitro, whereas MLK7‑AS1 overexpression exhibited opposite effects. Consistently, decreased MLK7‑AS1 expression inhibited tumor growth in vivo. Furthermore, RT‑qPCR and western blot assays revealed that p21 may be a potential downstream target of MLK7‑AS1. To the best of the authors' knowledge, this is the first study to report that MLK7‑AS1 has potential as a biomarker and may promote proliferation in CRC partially through downregulating p21 expression.

Clinical significance of serum DRAM1 mRNA, ARSA mRNA, hsa-miR-2053 and lncRNA-RP1-86D1.3 axis expression in malignant pleural mesothelioma

AIM AND BACKGROUND

Malignant pleural mesothelioma (MPM) is a lethal cancer mainly caused by chronic exposure of asbestos. In this pilot study, we aimed to assess the expression of serum RNA-based biomarker panel exploring their clinical utility as diagnostic and prognostic biomarkers for MPM.

METHODS

We have selected an MPM-specific RNA-based biomarker panel through bioinformatics analysis based on the integration of DNA damage regulated autophagy modulator 1 (DRAM1) and arylsulfatase A ( ARSA) gene expression with their epigenetic regulators microRNA ( miR-2053) and long noncoding RNA ( lncRNA-RP1-86D1.3). Then, quantitative real-time polymerase chain reaction (qPCR) validation in sera of 60 MPM patients, 20 chronic asbestos exposure patients, and 20 healthy volunteers was done. Lastly, the prognostic power of the selected panel was assessed.

RESULTS

The expression of serum DRAM1 messenger RNA (mRNA), ARSA mRNA, hsa-miR-2053 and lncRNA-RP1-86D1.3 were positive in 78.3%, 90%, 85%, and 83.3% of MPM patients, respectively. The RNA-based biomarker panel was able to discriminate between MPM patients and controls with high accuracy and their combined sensitivity reached 100% for the diagnosis of MPM. Kaplan-Meier analysis showed that hsa-miR-2053 is an independent prognostic factor of MPM.

CONCLUSION

Our preliminary data revealed that the chosen RNAs play an important role in driving MPM development and progression.

Long noncoding RNA MLK7-AS1 promotes ovarian cancer cells progression by modulating miR-375/YAP1 axis

Background

Long noncoding RNAs (LncRNAs) have been reported to be abnormally expressed in human ovarian cancer and associated with the proliferation and metastasis of cancer cells. The objective of this study was to investigate the role and the underlying mechanisms of LncRNA MAP3K20 antisense RNA 1 (MLK7-AS1) in ovarian cancer.

Methods

The expression level of MLK7-AS1 was investigated in human ovarian cancer tissues and cell lines. The effects of MLK7-AS1 knockdown on ovarian cancer cell proliferation, migration, invasion and apoptosis were evaluated in vitro using MTT, colony formation assays, wound healing assays, transwell assays and flow cytometry. Furthermore, the in vivo effects were determined using the immunodeficient NSG female mice. Luciferase reporter assays were employed to identify interactions among MLK7-AS1 and its target genes.

Results

In the current study, MLK7-AS1 was specifically upregulated in ovarian cancer tissues and cell lines. Knockdown of MLK7-AS1 inhibited the ability of cell migration, invasion, proliferation, colony formation and wound healing, whereas promoted cell apoptosis in vitro. By using online tools and mechanistic analysis, we demonstrated that MLK7-AS1 could directly bind to miR-375 and downregulate its expression. Besides, MLK7-AS1 reversed the inhibitory effect of miR-375 on the growth of ovarian cancer cells, which might be involved in the upregulation of Yes-associated protein 1 (YAP1) expression. Moreover, knockdown MLK7-AS1 expression inhibited primary tumor growth in ovary and metastatic tumors in multiple peritoneal organs including liver and spleen in vivo, which were partly abolished by miR-375 inhibition. Mechanically, we found that MLK7-AS1 modulated the epithelial-mesenchymal transition (EMT) process by interacting with miR-375/YAP1 both in vivo and vitro, which promoted the expression of Slug.

Conclusions

Taken together, our study showed for the first time that MLK7-AS1 interacted with miR-375 to promote proliferation, metastasis, and EMT process in ovarian cancer cells through upregulating YAP1.

Knockdown of long non-coding RNA MAP3K20 antisense RNA 1 inhibits gastric cancer growth through epigenetically regulating miR-375

Emerging evidence has demonstrated that long noncoding RNAs (lncRNAs) play a critical role in tumorigenesis of gastric cancer. LncRNA MAP3K20 antisense RNA 1 (MLK7-AS1) has been identified as one of gastric cancer-specific lncRNAs. However, its precise role in gastric cancer remains unknown. In this study, we found that lncRNA MLK7-AS1 was significantly increased in gastric cancer tissues compared with in adjacent tissues. Gastric cancer patients with high MLK7-AS1 expression had a shorter survival and poorer prognosis. By loss-function assay, we demonstrated that knockdown of MLK7-AS1 inhibited cell proliferation and induced apoptosis in HGC27and MKN-45 cells. Furthermore, we identified miR-375 as a target of MLK7-AS1. MLK7-AS1 interacted with Dnmt1 and recruited it to miR-375 promotor, hyper-methylating miR-375 promotor and repressing miR-375 expression. Taken together, our findings demonstrate that knockdown of MLK7-AS1 by siRNA inhibits gastric cancer growth by epigenetically regulating miR-375. Thus, MLK7-AS1 may be a useful prognostic marker and therapeutic target for gastric cancer patients.

lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs), which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA )RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC) method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes) that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

Construction and Comprehensive Analysis for Dysregulated Long Non-Coding RNA (lncRNA)-Associated Competing Endogenous RNA (ceRNA) Network in Gastric Cancer

Long non-coding RNA (lncRNA) is a kind of non-coding RNA with transcripts more than 200 bp in length. LncRNA can interact with the miRNA as a competing endogenous RNA (ceRNA) to regulate the expression of target genes, which play a significant role in the initiation and progression of tumors. In this study, we explored the functional roles and regulatory mechanisms of lncRNAs as ceRNAs in gastric cancer, and their potential implications for prognosis. The lncRNAs, miRNAs, and mRNAs expression profiles of 375 gastric cancer tissues and 32 non-tumor gastric tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the DESeq package. Survival analysis was estimated based on Kaplan-Meier curve analysis. KEGG pathway analysis was performed using KOBAS 3.0. The dysregulated lncRNA-associated ceRNA network was constructed in gastric cancer based on bioinformatics generated from miRcode and miRTarBase. A total of 237 differentially expressed lncRNAs and 198 miRNAs between gastric cancer and matched normal tissues were screened in our study with thresholds of |log2FC| >2 and adjusted P value <0.01. Eleven discriminatively expressed lncRNAs may be correlated with tumorigenesis of gastric cancer. Seven out of 11 dysregulated lncRNA were found to be significantly associated with overall survival in gastric cancer (P value <0.05). The newly identified ceRNA network includes 11 gastric cancer-specific lncRNAs, 9 miRNAs, and 41 mRNAs. Collectively, our study will contribute to improving the understanding of the lncRNA-associated ceRNA network regulatory mechanisms in the pathogenesis of gastric cancer and provide and identify novel lncRNAs as candidate prognostic biomarkers or potential therapeutic targets.

Genome-Wide Analysis of Uveal Melanoma Metastasis-Associated LncRNAs and Their Functional Network

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Up to 50% of primary UM cases will develop distant metastasis, but no effective therapies are currently available. The present study aimed to characterize the expression profile of the long noncoding RNAs (lncRNAs) and screen the potential metastasis-associated lncRNAs in UM. A genome-wide analysis of the transcriptome was performed on 11 primary UM tissues (6 metastasized and 5 nonmetastasized) through RNA sequencing. A total of 40,878 lncRNAs were detected in UM, 4,983 of which were novel candidates. We identified 329 differentially expressed lncRNAs (DELs) and 802 differentially expressed mRNAs (DEMs) by comparing the transcriptome profile between metastasized and nonmetastasized UM group. The DEL-DEM coexpression network revealed that the RP11-551L14.4, TCONS_00004101, and TCONS_00004845 DELs had the highest connectivity with the DEMs, coexpressed with 225, 28, and 10 DEMs, respectively, whereas the SPOCD1, PEA15, and SLC44A3 DEMs were most closely connected with the DELs, and were coexpressed with 89, 27, and 22 DELs, respectively. Moreover, 17 and 743 DEMs were targeted by the DELs through cis- or trans-action, respectively. These targeted DEMs were significantly enriched in D-Arginine and D-ornithine metabolism and glycerolipid metabolism of Kyoto Encyclopedia of Genes and Genomes pathways, and enriched in bradykinin receptor activity and haptoglobin binding of gene ontology biological processes. Quantitative real-time PCR confirmed the sequencing data. These findings have provided new insights into the molecular mechanism of UM metastasis and paved the way for further investigations regarding lncRNA in UM.

(-)-Epigallocatechin-3-gallate and atorvastatin treatment down-regulates liver fibrosis-related genes in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) and associated advanced liver diseases have become prevalent conditions in many countries and are associated with increased mortality. Gene expression profiles in NAFLD have been examined recently but changes in expression elicited by chemical compound treatments have not been investigated. Since (-)-Epigallocatechin-3-gallate (EGCG) and atorvastatin (ATST) exhibit similar efficacy in NAFLD models, we reasoned that some common key genes might alter after treatment of EGCG and ATST. Accordingly, we applied integrated bioinformatics analyses of RNA microarray data from EGCG and ATST treatment groups compared to controls in a NAFLD phenotypic mouse model. Using differential expression (DE) analysis, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA) and ClueGO enrichment, shared EGCG and ATST down-regulated pathways were identified which included extracellular matrix (ECM)-receptor interaction and protein processing in endoplasmic reticulum (ER). To refine key genes associated with liver fibrosis, a human NAFLD signature derived from patients of different fibrosis stages was analyzed. The results showed that fibrosis-related genes Col1a1, Col1a2, Col3a1 and Col6a3 were significantly down-regulated. These four genes were further validated as down-regulated in an independent mouse NAFLD dataset. We conclude that EGCG and ATST treatment results in the significant down-regulation of genes related to liver fibrosis.

Comprehensive analysis of a novel four-lncRNA signature as a prognostic biomarker for human gastric cancer

Emerging evidence indicates that long non-coding RNAs (lncRNAs) play a crucial role in predicting survival for gastric cancer (GC) patients. This study aims to identify a lncRNA-related signature for evaluating the overall survival of 379 GC patients from The Cancer Genome Atlas (TCGA) database. The associations between survival outcome and the expression of lncRNAs were evaluated by the univariate and multivariate Cox proportional hazards regression analyses. Four lncRNAs (LINC01018, LOC553137, MIR4435-2HG, and TTTY14) were identified as significantly correlated with overall survival. These four lncRNAs were gathered as a single prognostic signature. There was a significant positive correlation between GC patients with low-risk scores and overall survival (P = 0.001). Further analysis suggested that the prognostic value of this four-lncRNA signature was independent in clinical features. Gene set enrichment analysis found that these four lncRNAs were correlated with several molecular pathways of the tumor. Our study indicates that this novel lncRNA expression signature may be a useful biomarker of the prognosis for GC patients, based on bioinformatics analysis.

Identification of four prognostic LncRNAs for survival prediction of patients with hepatocellular carcinoma

Background

As the fifth most common cancer worldwide, Hepatocellular carcinoma (HCC) is also the third most common cause of cancer-related death in China. Several lncRNAs have been demonstrated to be associated with occurrence and prognosis of HCC. However, identification of prognostic lncRNA signature for HCC with expression profiling data has not been conducted yet.

Methods

With the reuse of public available TCGA data, expression profiles of lncRNA for 371 patients with HCC were obtained and analyzed to find the independent prognostic lncRNA. Based on the expression of lncRNA, we developed a risk score model, which was evaluated by survival analysis and ROC (receiver operating characteristic) curve. Enrichment analysis was performed to predict the possible role of the identified lncRNA in HCC prognosis.

Results

Four lncRNAs (RP11-322E11.5, RP11-150O12.3, AC093609.1, CTC-297N7.9) were found to be significantly and independently associated with survival of HCC patients. We used these four lncRNAs to construct a risk score model, which exhibited a strong ability to distinguish patients with significantly different prognosis (HR = 2.718, 95% CI [2.103–3.514], p = 2.32e−14). Similar results were observed in the subsequent stratification survival analysis for HBV infection status and pathological stage. The ROC curve also implied our risk score as a good indicator for 5-year survival prediction. Furthermore, enrichment analysis revealed that the four signature lncRNAs may be involved in multiple pathways related to tumorigenesis and prognosis.

Discussion

Our study recognized four lncRNAs to be significantly associated with prognosis of liver cancer, and could provide novel insights into the potential mechanisms of HCC progression. Additionally, CTC-297N7.9 may influence the downstream TMEM220 gene expression through cis-regualtion. Nevertheless, further well-designed experimental studies are needed to validate our findings.

Investigation of a common gene expression signature in gastrointestinal cancers using systems biology approaches

According to GLOBOCAN 2012, the incidence and the mortality rate of colorectal, stomach and liver cancers are the highest among the total gastrointestinal (GI) cancers.

Identification of a RNA-Seq Based 8-Long Non-Coding RNA Signature Predicting Survival in Esophageal Cancer

Background

Accumulating evidence suggests the involvement of long non-coding RNAs (lncRNAs) as oncogenic or tumor suppressive regulators in the development of various cancers. In the present study, we aimed to identify a lncRNA signature based on RNA sequencing (RNA-seq) data to predict survival in esophageal cancer.

Material/Methods

The RNA-seq lncRNA expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs were screened out between esophageal cancer and normal tissues. Univariate and multivariate Cox regression analysis were performed to establish a lncRNA-related prognostic model. Receiver operating characteristic (ROC) analysis was conducted to test the sensitivity and specificity of the model. GO (gene ontology) functional and KEGG pathway enrichment analyses were performed for mRNAs co-expressed with the lncRNAs to explore the potential functions of the prognostic lncRNAs.

Results

A total of 265 differentially expressed lncRNAs were identified between esophageal cancer and normal tissues. After univariate and multivariate Cox regression analysis, eight lncRNAs (GS1-600G8.5, LINC00365, CTD-2357A8.3, RP11-705O24.1, LINC01554, RP1-90J4.1, RP11-327J17.1, and LINC00176) were finally screened out to establish a predictive model by which patients could be classified into high-risk and low-risk groups with significantly different overall survival. Further analysis indicated independent prognostic capability of the 8-lncRNA signature from other clinicopathological factors. ROC curve analysis demonstrated good performance of the 8-lncRNA signature. Functional enrichment analysis showed that the prognostic lncRNAs were mainly associated with esophageal cancer related biological processes such as regulation of glucose metabolic process and amino acid and lipids metabolism.

Conclusions

Our study developed a novel candidate model providing additional and more powerful prognostic information beyond conventional clinicopathological factors for survival prediction of esophageal cancer patients. Moreover, it also brings us new insights into the molecular mechanisms underlying esophageal cancer.