A three-lncRNA signature predicts overall survival and disease-free survival in patients with esophageal squamous cell carcinoma

A nomogram based on 4-lncRNAs signature for improving prognostic prediction of hepatocellular carcinoma

PURPOSE

Long noncoding RNAs (lncRNAs) with abnormal expression are frequently seen in hepatocellular cancer patients (HCC). Previous studies have reported the correlation between lncRNA and prognosis processes of HCC patients. In this research, a graphical nomogram with lncRNAs signatures, T, M phases was developed using the rms R package to estimate the survival rates of HCC patients in year 1, 3, and 5.

METHODS

To find the prognostic lncRNA and create the lncRNA signatures, univariate Cox survival analysis and multivariate Cox regression analysis were chosen. The rms R software package was used to build a graphical nomogram based on lncRNAs signatures to predict the survival rates in of HCC patients in 1, 3, and 5 years. Using "edgeR", "DEseq" R packages to find the differentially expressed genes (DEGs).

RESULTS

Firstly, a total of 5581 DEGs including 1526 lncRNAs and 3109 mRNAs were identified through bioinformatic analysis, of which 4 lncRNAs (LINC00578, RP11-298O21.2, RP11-383H13.1, RP11-440G9.1) were identified to be strongly related to the prognosis of liver cancer (P < 0.05). Moreover, we constructed a 4-lncRNAs signature by using the calculated regression coefficient. 4-lncRNAs signature is identified to significantly correlated with clinical and pathological characteristics (such as T stage, and death status of HCC patients).

CONCLUSIONS

A prognostic nomogram on the base of 4-lncRNAs markers was built, which is capable to accurately predict the 1-year, 3-year, and 5-year survival of HCC patients after the construction of the 4-lncRNAs signature linked with prognosis of HCC.

Identification and validation of a novel angiogenesis-related gene signature for predicting prognosis in gastric adenocarcinoma

Background

Angiogenesis is a major promotor of tumor progression and metastasis in gastric adenocarcinoma (STAD). We aimed to develop a novel lncRNA gene signature by identifying angiogenesis-related genes to better predict prognosis in STAD patients.

Methods

The expression profiles of angiogenesis-related mRNA and lncRNA genes were collected from The Cancer Genome Atlas (TCGA). Then, the "limma" package was used to identify differentially expressed genes (DEGs). The expression profiles of angiogenesis-related genes were clustered by consumusclusterplus. The Pearson correlation coefficient was further used to identify lncRNAs coexpressed with angiogenesis-related clustere genes. We used Lasso Cox regression analysis to construct the angiogenesis-related lncRNAs signature. Furthermore, the diagnostic accuracy of the prognostic risk signature were validated by the TCGA training set, internal test sets and external test set. We used multifactor Cox analysis to determine that the risk score is an independent prognostic factor different from clinical characteristics. Nomogram has been used to quantitatively determine personal risk in a clinical environment. The ssGSEA method or GSE176307 data were used to evaluate the infiltration state of immune cells or predictive ability for the benefit of immunotherapy by angiogenesis-related lncRNAs signature. Finally, the expression and function of these signature genes were explored by RT-PCR and colony formation assays.

Results

Among angiogenesis-related genes clusters, the stable number of clusters was 2. A total of 289 DEGs were identified and 116 lncRNAs were screened to have a significant coexpression relationship with angiogenic DEGs (P value<0.001 and |R| >0.5). A six-gene signature comprising LINC01579, LINC01094, RP11.497E19.1, AC093850.2, RP11.613D13.8, and RP11.384P7.7 was constructed by Lasso Cox regression analysis. The multifactor Cox analysis and Nomogram results showed that our angiogenesis-related lncRNAs signature has good predictive ability for some different clinical factors. For immune, angiogenesis-related lncRNAs signature had the ability to efficiently predict infiltration state of 23 immune cells and immunotherapy. The qPCR analysis showed that the expression levels of the six lncRNA signature genes were all higher in gastric adenocarcinoma tissues than in adjacent tissues. The functional experiment results indicated that downregulation of the expression of these six lncRNA signature genes suppressed the proliferation of ASG and MKN45 cells.

Conclusion

Six angiogenesis-related genes were identified and integrated into a novel risk signature that can effectively assess prognosis and provide potential therapeutic targets for STAD patients.

Regulatory Roles of Noncoding RNAs in the Progression of Gastrointestinal Cancers and Health Disparities

Annually, more than a million individuals are diagnosed with gastrointestinal (GI) cancers worldwide. With the advancements in radio- and chemotherapy and surgery, the survival rates for GI cancer patients have improved in recent years. However, the prognosis for advanced-stage GI cancers remains poor. Site-specific GI cancers share a few common risk factors; however, they are largely distinct in their etiologies and descriptive epidemiologic profiles. A large number of mutations or copy number changes associated with carcinogenesis are commonly found in noncoding DNA regions, which transcribe several noncoding RNAs (ncRNAs) that are implicated to regulate cancer initiation, metastasis, and drug resistance. In this review, we summarize the regulatory functions of ncRNAs in GI cancer development, progression, chemoresistance, and health disparities. We also highlight the potential roles of ncRNAs as therapeutic targets and biomarkers, mainly focusing on their ethnicity-/race-specific prognostic value, and discuss the prospects of genome-wide association studies (GWAS) to investigate the contribution of ncRNAs in GI tumorigenesis.

Oncogenic roles of the lncRNA LINC00460 in human cancers

Long noncoding RNAs (lncRNAs) represent an important group of endogenous RNAs with limit protein-encoding capability, with a length of more than 200 nucleotides. Emerging evidence have demonstrated that lncRNAs are greatly involved in multiple cancers by playing critical roles in tumor initiation and progression. Long intergenic non-protein coding RNA 460 (LINC00460), a novel cancer-related lncRNA, exhibits abnormal expression and oncogenic function in multiple cancers, and positively correlates with poor clinical characteristics of cancer patients. LINC00460 has also been shown to be a promising biomarker for diagnosis as well as prognostic evaluation in cancer patients. In this review, we briefly summarized recent knowledge on the expression, functional roles, molecular mechanisms, and diagnostic and prognostic values of LINC00460 in human malignancies.

Lipid metabolism-related lncRNA SLC25A21-AS1 promotes the progression of oesophageal squamous cell carcinoma by regulating the NPM1/c-Myc axis and SLC25A21 expression

BACKGROUND

Obesity alters metabolic microenvironment and is thus associated with several tumours. The aim of the present study was to investigate the role, molecular mechanism of action, and potential clinical value of lipid metabolism-related long non-coding RNA (lncRNA) SLC25A21-AS1 in oesophageal squamous cell carcinoma (ESCC).

METHODS

A high-fat diets (HFDs)-induced obesity nude mouse model was established, and targeted metabolomics analysis was used to identify critical medium-long chain fatty acids influencing the growth of ESCC cells. Transcriptomic analysis of public dataset GSE53625 confirmed that lncRNA SLC25A21-AS1 was a lipid metabolism-related lncRNA. The biological function of lncRNA SLC25A21-AS1 in ESCC was investigated both in vivo and in vitro. Chromatin immunoprecipitation(ChIP)assay, RNA-pull down, mass spectrometry, co-IP, and RNA IP(RIP) were performed to explore the molecular mechanism. Finally, an ESCC cDNA microarray was used to determine the clinical prognostic value of SLC25A21-AS1 by RT-qPCR.

RESULTS

Palmitic acid (PA) is an important fatty acid component of HFD and had an inhibitory effect on ESCC cell lines. LncRNA SLC25A21-AS1 expression was downregulated by PA and associated with the proliferation and migration of ESCC cells in vitro and in vivo. Mechanistically, SLC25A21-AS1 interacted with nucleophosmin-1 (NPM1) protein to promote the downstream gene transcription of the c-Myc in the nucleus. In the cytoplasm, SLC25A21-AS1 maintained the stability of SLC25A21 mRNA and reduced the intracellular NAD+ /NADH ratio by influencing tryptophan catabolism. Finally, we demonstrated that high expression of SLC25A21-AS1 promoted resistance to cisplatin-induced apoptosis and was correlated with poor tumour grade and overall survival.

CONCLUSIONS

HFD/PA has an inhibitory effect on ESCC cells and SLC25A21-AS1 expression. SLC25A21-AS1 promotes the proliferation and migration of ESCC cells by regulating the NPM1/c-Myc axis and SLC25A21 expression. In addition, lncRNA SLC25A21-AS1 may serve as a favourable prognostic biomarker and a potential therapeutic target for ESCC.

Prognostic Nomogram for Predicting Long-Term Overall Survival of Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiotherapy Plus Surgery: A Population-Based Study

Objective

Neoadjuvant chemoradiotherapy (nCRT) plays an important role in patients with locally advanced esophageal cancer (EC). We aim to determine the prognostic risk factors and establish a reliable nomogram to predict overall survival (OS) based on SEER population.

Methods

Patients with EC coded by 04–15 in the SEER database were included. The data were divided into training group and verification group (7:3). The Cox proportional-risk model was evaluated by using the working characteristic curve (receiver operating characteristic curve, ROC) and the area under the curve (AUC), and a nomogram was constructed. The calibration curve was used to measure the consistency between the predicted and the actual results. Decision curve analysis (DCA) was used to evaluate its clinical value. The best cut-off value of nomogram score in OS was determined by using X-tile software, and the patients were divided into low-risk, medium-risk, and high-risk groups.

Results

A total of 2,209 EC patients who underwent nCRT were included in further analysis, including 1,549 in the training cohort and 660 in the validation group. By Cox analysis, sex, marital status, T stage, N stage, M stage, and pathological grade were identified as risk factors. A nomogram survival prediction model was established to predict the 36-, 60-, and 84-month survival. The ROC curve and AUC showed that the model had good discrimination ability. The correction curve was in good agreement with the prediction results. DCA further proved the effective clinical value of the nomogram model. The results of X-tile analysis showed that the long-term prognosis of patients in the low-risk subgroup was better in the training cohort and the validation cohort (p &lt; 0.001).

Conclusion

This study established an easy-to-use nomogram risk prediction model consisting of independent prognostic factors in EC patients receiving nCRT, helping to stratify risk, identify high-risk patients, and provide personalized treatment options.

Identification and validation of an eight-lncRNA signature that predicts prognosis in patients with esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is correlated with worse clinical prognosis and lacks available targeted therapy. Thus, identification of reliable biomarkers is required for the diagnosis and treatment of ESCC.

Methods

We downloaded the GSE53625 dataset as a training dataset to screen differentially expressed RNAs (DERs) with the criterion of false discovery rate (FDR) < 0.05 and |log₂fold change (FC)| > 1. A support vector machine classifier was used to find the optimal feature gene set that could conclusively distinguish different samples. An eight-lncRNA signature was identified by random survival forest algorithm and multivariate Cox regression analysis. The RNA sequencing data from The Cancer Genome Atlas (TCGA) database were used for external validation. The predictive value of the signature was assessed using Kaplan–Meier test, time-dependent receiver operating characteristic (ROC) curves, and dynamic area under the curve (AUC). Furthermore, a nomogram to predict patients’ 3-year and 5-year prognosis was constructed. CCK-8 assay, flow cytometry, and transwell assay were conducted in ESCC cells.

Results

A total of 1136 DERs, including 689 downregulated mRNAs, 318 upregulated mRNAs, 74 downregulated lncRNAs and 55 upregulated lncRNAs, were obtained in the GES53625 dataset. From the training dataset, we identified an eight-lncRNA signature, (ADAMTS9-AS1, DLX6-AS1, LINC00470, LINC00520, LINC01497, LINC01749, MAMDC2-AS1, and SSTR5-AS1). A nomogram based on the eight-lncRNA signature, age, and pathologic stage was developed and showed good accuracy for predicting 3-year and 5-year survival probability of patients with ESCC. Functionally, knockdown of LINC00470 significantly suppressed cell proliferation, G1/S transition, and migration in two ESCC cell lines (EC9706 and TE-9). Moreover, knockdown of LINC00470 downregulated the protein levels of PCNA, CDK4, and N-cadherin, while upregulating E-cadherin protein level in EC9706 and TE-9 cells.

Conclusion

Our eight-lncRNA signature and nomogram can provide theoretical guidance for further research on the molecular mechanism of ESCC and the screening of molecular markers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11658-022-00331-x.

An Immunity-Associated lncRNA Signature for Predicting Prognosis in Gastric Adenocarcinoma

Background

Gastric adenocarcinoma (GAD) is one of the most common tumors in the world and the prognosis is still very poor.

Objective

We sought to identify reliable prognostic biomarkers for the progression of GAD and the sensitivity to drug therapy.

Method

The RNA sequencing data of GAD was downloaded from the Cancer Genome Atlas (TCGA) database and used for analysis. Differentially expressed, immune-related lncRNA (DEIRlncRNA) was characterized by differential analysis and correlation analysis. Univariate Cox regression analysis was used to identify DEIRlncRNA associated with prognosis. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed us to determine a signature composed of eight IRlncRNAs. Based on this signature, we further performed gene set enrichment analysis (GSEA) and somatic mutation analysis to evaluate the ability of this signature to predict prognosis.

Results

In total, 72 immune-related lncRNAs (DEIRlncRNAs) with prognostic value were identified. These lncRNAs were used to construct a model containing eight immune-related lncRNAs (8-IRlncRNAs). Based on this risk model, we divided GAD patients into high-risk and low-risk groups. The analysis showed that the prognosis of the two groups was different and that the high-risk group had worse overall survival (OS). Immune cell infiltration analysis showed that the proportion of memory B cells increased in the high-risk group while the proportion of macrophages M1, T cells, CD4 memory-activated cells, and T cell follicular helpers decreased. GSEA results showed that 8-IRlncRNA was significantly enriched in tumorigenesis pathways such as myc. The results of somatic mutation analysis showed that the CDH1 gene was significantly mutated in the high-risk group.

Conclusion

A prognostic signature of 8-IRlncRNAs in GAD was established and this signature was able to predict the prognosis of GAD patients.

Integrative bioinformatics analysis identifies LINC01614 as a potential prognostic signature in esophageal cancer

Background

Esophageal cancer (EC) is one of the most common gastrointestinal cancers and the incidence is on the increase in recent years. The aim of the present study was to assess novel long non-coding RNA (lncRNA) biomarkers for the prognosis of EC through the analysis of gene expression microarrays.

Methods

Three datasets (GSE53622, GSE53624, and GSE53625) were downloaded from the Gene Expression Omnibus (GEO) database and EC patients’ clinical information were from The Cancer Genome Atlas (TCGA) databases. Differentially expressed genes (DEGs) were screened by comparing tumor tissues with normal tissues using limma R package. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database was used to obtain the novel lncRNAs and their co-expression genes in EC and these were visualized with the Cytoscape software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) database was used to analyze the functions enrichment of selected DEGs. Cell Counting Kit-8 (CCK8) and Transwell assays were used to further confirm the function of target lncRNAs.

Results

We identified 24 differentially expressed (DE) lncRNAs and 659 DE mRNAs from the intersection of GEO and TCGA databases. And we found that only LINC01614 was concerned with a candidate prognostic signature in EC. “Extracellular matrix (ECM)-receptor interaction” and “PI3K-Akt signaling pathway” were observed, and we constructed a lncRNA-mRNA co-expression network for EC that includes LINC01614 and 64 mRNAs. The results of CCK8 and Transwell assays showed that suppression of LINC01614 inhibited EC cell proliferation and migration.

Conclusions

Our study might provide LINC01614 as a novel lncRNA biomarker for diagnosis and prognosis in EC.

Development of a Gene Risk Signature for Patients of Pancreatic Cancer

BACKGROUND

Pancreatic cancer is a highly malignant solid tumor with a high lethality rate, but there is a lack of clinical biomarkers that can assess patient prognosis to optimize treatment.

METHODS

Gene-expression datasets of pancreatic cancer tissues and normal pancreatic tissues were obtained from the GEO database, and differentially expressed genes analysis and WGCNA analysis were performed after merging and normalizing the datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were used to screen the prognosis-related genes in the modules with the strongest association with pancreatic cancer and construct risk signatures. The performance of the risk signature was subsequently validated by Kaplan-Meier curves, receiver operating characteristic (ROC), and univariate and multivariate Cox analyses.

RESULT

A three-gene risk signature containing CDKN2A, BRCA1, and UBL3 was established. Based on KM curves, ROC curves, and univariate and multivariate Cox regression analyses in the TRAIN cohort and TEST cohort, it was suggested that the three-gene risk signature had better performance in predicting overall survival.

CONCLUSION

This study identifies a three-gene risk signature, constructs a nomogram that can be used to predict pancreatic cancer prognosis, and identifies pathways that may be associated with pancreatic cancer prognosis.

LncRNA LINC00460: Function and mechanism in human cancer

Long non-coding RNAs (LncRNAs), which are more than 200 nucleotides in length and with limited protein-coding potential, play vital roles in the pathogenesis, tumorigenesis, and angiogenesis of cancers. Aberrant expression of lncRNAs has been detected in various carcinomas and may be correlated with oncogenesis by affecting related genes expression. Recently, an increasing number of studies have reported on long intergenic non-protein coding RNA 460 (LINC00460) in human tumor fields. LINC00460 is upregulated in diverse cancer tissues and cells. The upregulated expression level of LINC00460 is correlated with larger tumor size, tumor node metastasis (TNM) stage, lymph node metastasis, and shorter overall survival. The regulatory mechanism of LINC00460 was complex and diverse. LINC00460 could act as a competitive endogenous RNA (ceRNA), directly bind with proteins or regulate multiple pathways, which affected tumor progression. Moreover, LINC00460 was also identified to increase drug resistance, and therefore, weaken the effectiveness of tumor treatment. It has become increasingly important to investigate the roles of LINC00460 in various cancers by different mechanisms. Therefore, a more comprehensive understanding of LINC00460 is crucial to expound on the cellular function and molecular mechanism of human cancers. In this review, we refer to studies concerning LINC00460 and provide the basis for the evaluation of LINC00460 as a predicted biomarker or potential therapeutic target in malignancies, and also provide ideas for the future research of lncRNAs similar to LINC00460.

A novel ferroptosis-related lncRNA signature for prognosis prediction in gastric cancer

Background

Gastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC.

Methods

Ferroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model.

Results

We constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410–2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR.

Conclusions

This risk model was shown to be a novel method for predicting prognosis for GC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08975-2.

Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed an existing certain curative effect in treating PDAC. The aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients.

Method

In this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO (Gene Expression Omnibus) datasets, and identify modules related to CD4+ T cells. LASSO Cox regression analysis and Kaplan-Meier curve were applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and the Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analyses were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset.

Results

A four-gene signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verified by a Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset.

Conclusion

We constructed a novel four-gene signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC patients.

LncRNA RCAT1 promotes tumor progression and metastasis via miR-214-5p/E2F2 axis in renal cell carcinoma

Renal cell carcinoma is the second malignant tumors in the urinary system with high mortality and morbidity. Increasing evidence suggests that long non-coding RNAs (lncRNAs) play critical roles in tumor development and progression. In the current study, based on the publicly available data obtained from GEO and TCGA database, we identified five prognosis-related lncRNAs with the ability to predict the prognosis of patients with renal cell carcinoma. Among them, the uncharacterized and upregulated lncRNA RCAT1 (renal cancer-associated transcript 1) was identified as the key lncRNA. Our data further revealed that the expression of lncRNA RCAT1 was significantly upregulated in renal cell carcinoma tissues and cells. Gain-of-function and loss-of-function studies showed that lncRNA RCAT1 promoted cell proliferation, migration, and invasion in vitro and in vivo. Furthermore, we verified that lncRNA RCAT1 could abundantly sponge miR-214-5p, which served as a tumor suppressor in renal cell carcinoma. Significantly, miR-214-5p overexpression could attenuate the promotion of cell proliferation and metastasis induced by lncRNA RCAT1. Moreover, we found that E2F2 was a direct target of miR-214-5p, and lncRNA RCAT1 could protect E2F2 from miR-214-5p-mediated degradation. Taken together, our findings suggested that lncRNA RCAT1 could enhance the malignant phenotype of renal cell carcinoma cells by modulating miR-214-5p/E2F2 axis, and lncRNA RCAT1 might be a novel prognostic biomarker and a potential therapeutic target for renal cell carcinoma.

LINC00460 Is a Dual Biomarker That Acts as a Predictor for Increased Prognosis in Basal-Like Breast Cancer and Potentially Regulates Immunogenic and Differentiation-Related Genes

Breast cancer (BRCA) is a serious public health problem, as it is the most frequent malignant tumor in women worldwide. BRCA is a molecularly heterogeneous disease, particularly at gene expression (mRNAs) level. Recent evidence shows that coding RNAs represent only 34% of the total transcriptome in a human cell. The rest of the 66% of RNAs are non−coding, so we might be missing relevant biological, clinical or regulatory information. In this report, we identified two novel tumor types from TCGA with LINC00460 deregulation. We used survival analysis to demonstrate that LINC00460 expression is a marker for poor overall (OS), relapse-free (RFS) and distant metastasis-free survival (DMFS) in basal-like BRCA patients. LINC00460 expression is a potential marker for aggressive phenotypes in distinct tumors, including HPV-negative HNSC, stage IV KIRC, locally advanced lung cancer and basal-like BRCA. We show that the LINC00460 prognostic expression effect is tissue-specific, since its upregulation can predict poor OS in some tumors, but also predicts an improved clinical course in BRCA patients. We found that the LINC00460 expression is significantly enriched in the Basal-like 2 (BL2) TNBC subtype and potentially regulates the WNT differentiation pathway. LINC00460 can also modulate a plethora of immunogenic related genes in BRCA, such as SFRP5, FOSL1, IFNK, CSF2, DUSP7 and IL1A and interacts with miR-103-a-1, in-silico, which, in turn, can no longer target WNT7A. Finally, LINC00460:WNT7A ratio constitutes a composite marker for decreased OS and DMFS in Basal-like BRCA, and can predict anthracycline therapy response in ER-BRCA patients. This evidence confirms that LINC00460 is a master regulator in BRCA molecular circuits and influences clinical outcome.

Classifying gastric cancer using FLORA reveals clinically relevant molecular subtypes and highlights LINC01614 as a biomarker for patient prognosis

Molecular-based classifications of gastric cancer (GC) were recently proposed, but few of them robustly predict clinical outcomes. While mutation and expression signature of protein-coding genes were used in previous molecular subtyping methods, the noncoding genome in GC remains largely unexplored. Here, we developed the fast long-noncoding RNA analysis (FLORA) method to study RNA sequencing data of GC cases, and prioritized tumor-specific long-noncoding RNAs (lncRNAs) by integrating clinical and multi-omic data. We uncovered 1235 tumor-specific lncRNAs, based on which three subtypes were identified. The lncRNA-based subtype 3 (L3) represented a subgroup of intestinal GC with worse survival, characterized by prevalent TP53 mutations, chromatin instability, hypomethylation, and over-expression of oncogenic lncRNAs. In contrast, the lncRNA-based subtype 1 (L1) has the best survival outcome, while LINC01614 expression further segregated a subgroup of L1 cases with worse survival and increased chance of developing distal metastasis. We demonstrated that LINC01614 over-expression is an independent prognostic factor in L1 and network-based functional prediction implicated its relevance to cell migration. Over-expression and CRISPR-Cas9-guided knockout experiments further validated the functions of LINC01614 in promoting GC cell growth and migration. Altogether, we proposed a lncRNA-based molecular subtype of GC that robustly predicts patient survival and validated LINC01614 as an oncogenic lncRNA that promotes GC proliferation and migration.

Construction of a prognostic value model in papillary renal cell carcinoma by immune-related genes

Papillary renal cell carcinoma (PRCC) is the second most common type of renal carcinoma following clear cell renal cell carcinoma, and the role of immune-related genes (IRGs) in tumorigenesis and metastasis is evident; its prognostic value in PRCC remains unclear. In this study, we downloaded the gene expression profiles and clinical data of patients with PRCC from The Cancer Genome Atlas (TCGA) database and obtained IRGs from the ImmPort database. A total of 371 differentially expressed IRGs (DEIRGs) were discovered between PRCC and normal kidney tissues. Prognostic DEIRGs (PDEIRGs) were identified by univariate Cox regression analysis. Then, we screened the four most representative PDEIRGs (IL13RA2, CCL19, BIRC5, and INHBE) and used them to construct a risk model to predict the prognosis of patients with PRCC. This model precisely stratified survival outcome and accurately identified mutation burden in PRCC. Thus, our results suggest that these four PDEIRGs are available prognostic predictors for PRCC. They could be used to assess the prognosis and to guide individualized treatments for patients with PRCC.

Multifaceted Roles of Long Non-coding RNAs in Head and Neck Cancer

The majority of RNA transcripts are non-coding RNA (ncRNA) transcripts with lengths exceeding 200 nucleotides that are not translated into protein. Unlike microRNAs (miRNAs), long ncRNAs (lncRNAs) are not confined to a single mechanism of action but have a large and diverse role in biological processes as they can function as transcription regulators, decoys, scaffolds, and enhancer RNAs. Currently, many lncRNA molecules are under investigation for their role in tumorigenesis, metastasis, and prognosis in different types of cancer. This review not only summarizes the characteristics and functions of lncRNAs but also discusses the therapeutic implications and applications of lncRNAs with roles associated with head and neck cancer. Our aim is to pinpoint the potential way to perturb specific lncRNAs for future therapeutic use.

Six-gene-based prognostic model predicts overall survival in patients with uveal melanoma

BACKGROUND

Uveal melanoma (UM) is the most common primary intraocular tumor in adults, which has a high mortality rate and worse prognosis. Therefore, early potential molecular detection and prognostic evaluation seem more important for early diagnosis and treatment.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas-Uveal melanomas database. Survival genes were identified by univariate analysis and were regarded to be associated with the overall survival of UM patients. Then, pathway enrichment analysis of these survival genes was performed. Robust likelihood-based survival model and multivariate survival analysis were conducted to identify more reliable genes and the prognostic signature for UM survival prediction. Two internal datasets and another two UM datasets from Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.

RESULTS

Firstly, 2,010 survival genes were screened by univariate survival analysis. GO and KEGG analysis revealed that these genes were mainly involved in pathways such as mRNA processing, RNA splicing, spliceosome and ubiquitin mediated proteolysis. Secondly, a six-gene signature was identified by Robust likelihood-based survival model approach. The gene expression of the six genes can successfully divide UM samples into high- and low-risk groups and have strong survival prediction ability. What's more, the expression of six genes was compared in 80 healthy adipose tissue samples obtained from GTEx (Genotype-Tissue Expression) database and further validated in internal datasets and GEO datasets, which also can predict UM patient survival.

CONCLUSIONS

The six genes (SH2D3A, TMEM201, LZTS1, CREG1, NIPA1 and HIST1H4E) model might play a vital role in prognosis of UM, which should be helpful for further insight into the treatment of uveal melanoma.

A Four-Methylated lncRNAs-Based Prognostic Signature for Hepatocellular Carcinoma

Currently, an increasing number of studies suggest that long non-coding RNAs (lncRNAs) and methylation-regulated lncRNAs play a critical role in the pathogenesis of various cancers including hepatocellular carcinoma (HCC). Therefore, methylated differentially expressed lncRNAs (MDELs) may be critical biomarkers of HCC. In this study, 63 MDELs were identified by screening The Cancer Genome Atlas (TCGA) HCC lncRNAs expression data set and lncRNAs methylation data set. Based on univariate and multivariate survival analysis, four MDELs (AC025016.1, LINC01164, LINC01183 and LINC01269) were selected to construct the survival prognosis prediction model. Through the PI formula, the study indicates that our new prediction model performed well and is superior to the traditional staging method. At the same time, compared with the previous prediction models reported in the literature, the results of time-dependent receiver operating characteristic (ROC) curve analysis show that our 4-MDELs model predicted overall survival (OS) stability and provided better prognosis. In addition, we also applied the prognostic model to Cancer Cell Line Encyclopedia (CCLE) cell lines and classified different hepatoma cell lines through the model to evaluate the sensitivity of different hepatoma cell lines to different drugs. In conclusion, we have established a new risk scoring system to predict the prognosis, which may have a very important guiding significance for the individualized treatment of HCC patients.

Using ESTIMATE algorithm to establish an 8-mRNA signature prognosis prediction system and identify immunocyte infiltration-related genes in Pancreatic adenocarcinoma

OBJECTIVE

The tumour microenvironment is one of the significant factors driving the carcinogenesis of Pancreatic adenocarcinoma (PAAD). However, the underlying mechanism of how the tumour microenvironment impacts the prognosis of PAAD is not completely clear.

RESULTS

The transcriptome and clinical data of 182 PAAD program cases were downloaded from the TCGA database. Three hundred thirty-three differentially expressed genes (DEGs) between high and low stromal groups and 314 DEGs between high and low immune score groups were identified using ESTIMATE score. Based on the 203 genes differentially expressed simultaneously in two score-related comparisons, we established an 8-mRNA signature to evaluate the prognosis of PAAD patients. Kaplan-Meier curves showed significantly worse survival for patients with high-risk scores in both the training and validation groups. The risk score was an independent prognostic factor and had a high predictive value for the prognosis of patients with PAAD. By searching the TCGA database, we showed that CA9, CXCL9, and GIMAP7 from the 8-mRNA signature were associated with the infiltration levels of immunocytes by regulating FOXO1 expression in PAAD.

CONCLUSIONS

Unlike traditional methods of screening for differential genes in cancer and healthy tissues, we constructed a novel 8-mRNA signature to predict the prognosis of PAAD patients by applying ESTIMATE scoring to RNA-seq-based transcriptome data. Most importantly, we identified CA9, CXCL9, and GIMAP7 from the above eight genes as regulators of immunocyte infiltration by adjusting the expression of FOXO1 in PAAD. Thus, CA9, CXCL9, and GIMAP7 might be the ideal targets of immune therapy of PAAD.

METHODS

ESTIMATE scoring was used to determine the stromal and immune scores of transcriptome datasets downloaded from the TCGA database. An mRNA-based prognostic signature was built for the training cohort via the LASSO Cox regression model. The signature was verified using a validation cohort. Kaplan-Meier curves and log-rank analysis were used to identify survival differences. Western blot analysis and RT-qPCR analysis were carried out to analyze the expression of specific proteins and mRNAs. IHC was performed to assess the protein levels of Forkhead box-O 1 (FOXO1), Carbonic anhydrase 9 (CA9), C-X-C motif chemokine ligand 9 (CXCL9), and GTPase, IMAP family member 7 (GIMAP7) in the tissue microarray of PAAD.

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma

Background: Metabolic syndrome (MetS) is associated with the development of esophageal squamous cell carcinoma (ESCC), and long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms of MetS and tumor. This study will explore the prognostic effect of MetS and the associated lncRNA signature on ESCC.

Methods: Our previous RNA-chip data (GSE53624, GSE53622) for 179 ESCC patients were reanalyzed according to MetS. The recurrence-free survival (RFS) was collected for these patients. The status of the MetS-related tumor microenvironment was analyzed with the CIBERSORT and ESTIMATE algorithms. A lncRNA signature was established with univariate and multivariate Cox proportional hazards regression (PHR) analysis and verified using the Kaplan–Meier survival curve analysis and time-dependent receiver operating characteristic (ROC) curves. A clinical predictive model was constructed based on multiple risk factors, evaluated using C-indexes and calibration curves, and verified using data from the GEO and TCGA databases.

Results: The results showed that MetS was an independent risk factor for ESCC patients conferring low OS and RFS. Tumor microenvironment analysis indicated that patients with MetS have high stromal scores and M2 macrophage infiltration. A six-lncRNA signature was established by 60 ESCC patients randomly selected from GSE53624 and identified with an effective predictive ability in validation cohorts (59 patients from GSE53624 and 60 patients from GSE53622), subgroup analysis, and ESCC patients from TCGA. MetS and the six-lncRNA signature could be regarded as independent risk factors and enhanced predictive ability in the clinical predictive model.

Conclusions: Our results indicated that MetS was associated with poor prognosis in ESCC patients, and the possible mechanism was related to changes in the tumor microenvironment. MetS and the six-lncRNA signature could also serve as independent risk factors with available clinical application value.

The cox-filter method identifies respective subtype-specific lncRNA prognostic signatures for two human cancers

BACKGROUND

The most common histological subtypes of esophageal cancer are squamous cell carcinoma (ESCC) and adenocarcinoma (EAC). It has been demonstrated that non-marginal differences in gene expression and somatic alternation exist between these two subtypes; consequently, biomarkers that have prognostic values for them are expected to be distinct. In contrast, laryngeal squamous cell cancer (LSCC) has a better prognosis than hypopharyngeal squamous cell carcinoma (HSCC). Likewise, subtype-specific prognostic signatures may exist for LSCC and HSCC. Long non-coding RNAs (lncRNAs) hold promise for identifying prognostic signatures for a variety of cancers including esophageal cancer and head and neck squamous cell carcinoma (HNSCC).

METHODS

In this study, we applied a novel feature selection method capable of identifying specific prognostic signatures uniquely for each subtype - the Cox-filter method - to The Cancer Genome Atlas esophageal cancer and HSNCC RNA-Seq data, with the objectives of constructing subtype-specific prognostic lncRNA expression signatures for esophageal cancer and HNSCC.

RESULTS

By incorporating biological relevancy information, the lncRNA lists identified by the Cox-filter method were further refined. The resulting signatures include genes that are highly related to cancer, such as H19 and NEAT1, which possess perfect prognostic values for esophageal cancer and HNSCC, respectively.

CONCLUSIONS

The Cox-filter method is indeed a handy tool to identify subtype-specific prognostic lncRNA signatures. We anticipate the method will gain wider applications.

Identification of a nomogram based on long non-coding RNA to improve prognosis prediction of esophageal squamous cell carcinoma

PURPOSE

Esophageal squamous cell carcinoma (ESCC) remains a common aggressive malignancy in the world. Several long non-coding RNAs (lncRNAs) are reported to predict the prognosis of ESCC. Therefore, an in-depth research is urgently needed to further investigate the prognostic value of lncRNAs in ESCC.

RESULTS

From the training set, we identified a eight-lncRNA signature (including AP000487, AC011997, LINC01592, LINC01497, LINC01711, FENDRR, AC087045, AC137770) which separated the patients into two groups with significantly different overall survival (hazard ratio, HR = 3.79, 95% confidence interval, 95% CI [2.56-5.62]; P < 0.001). The signature was applied to the validation set (HR = 2.73, 95%CI [1.65-4.53]; P < 0.001) and showed similar prognostic values. Stratified, univariate and multivariate Cox regression analysis indicated that the signature was an independent prognostic factor for patients with ESCC. A nomogram based on the lncRNAs signature, age, grade and stage was developed and showed good accuracy for predicting 1-, 3- and 5-year survival probability of ESCC patients. We found a strong correlation between the gene significance for the survival time and T stage. Eight modules were constructed, among which the key module most closely associated with clinical information was identified.

CONCLUSIONS

Our eight-lincRNA signature and nomogram could be practical and reliable prognostic tools for esophageal squamous cell carcinoma.

METHODS

We downloaded the lncRNA expression profiles of ESCC patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets and separated to training and validation cohort. The univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identify a lncRNA-based signature. The predictive value of the signature was assessed using the Kaplan-Meier method, receiver operating characteristic (ROC) curves and area under curve (AUC). Weighted gene co-expression network analysis (WGCNA) was applied to predict the intrinsic relationship between gene expressions. In addition, we further explored the combination of clinical information and module construction.

Long non-coding RNA, LINC01614 as a potential biomarker for prognostic prediction in breast cancer

Background

Dysregulated long non-coding RNAs (lncRNAs) may serve as potential biomarkers of cancers including breast cancer (BRCA). This study aimed to identify lncRNAs with strong prognostic value for BRCA.

Methods

LncRNA expression profiles of 929 tissue samples were downloaded from TANRIC database. We performed differential expression analysis between paired BRCA and adjacent normal tissues. Survival analysis was used to identify lncRNAs with prognostic value. Univariate and multivariate Cox regression analyses were performed to confirm the independent prognostic value of potential lncRNAs. Dysregulated signaling pathways associated with lncRNA expression were evaluated using gene set enrichment analysis.

Results

We found that a total of 398 lncRNAs were significantly differentially expressed between BRCA and adjacent normal tissues (adjusted P value <= 0.0001 and |logFC| >= 1). Additionally, 381 potential lncRNAs were correlated Overall Survival (OS) (P value < 0.05). A total of 48 lncRNAs remained when differentially expressed lncRNAs overlapped with lncRNAs that had prognostic value. Among the 48 lncRNAs, one lncRNA (LINC01614) had stronger prognostic value and was highly expressed in BRCA tissues. LINC01614 expression was validated as an independent prognostic factor using univariate and multivariate analyses. Higher LINC01614 expression was observed in several molecular subgroups including estrogen receptors+, progesterone receptors+ and human epidermal growth factor receptor 2 (HER2)+ subgroup, respectively. Also, BRCA carrying one of four gene mutations had higher expression of LINC01614 including AOAH, CIT, HER2 and ODZ1. Higher expression of LINC01614 was positively correlated with several gene sets including TGF-β1 response, CDH1 signals and cell adhesion pathways.

Conclusions

A novel lncRNA LINC01614 was identified as a potential biomarker for prognosis prediction of BRCA. This study emphasized the importance of LINC01614 and further research should be focused on it.

Integrated analysis highlights multiple long non‑coding RNAs and their potential roles in the progression of human esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a prevalent aggressive malignant tumor with poor prognosis. Investigations into the molecular changes that occur as a result of the disease, as well as identification of novel biomarkers for its diagnosis and prognosis, are urgently required. Long non‑coding RNAs (lncRNAs) have been reported to play a critical role in tumor progression. The present study performed data mining analyses for ESCC via an integrated study of accumulated datasets and identification of the differentially expressed lncRNAs from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The identified intersection of differentially expressed genes (lncRNAs, miRNAs and mRNAs) in ESCC tissues between the GEO and TCGA datasets was investigated. Based on these intersected lncRNAs, the present study constructed a competitive endogenous RNA (ceRNA) network of lncRNAs in ESCC. A total of 81 intersection lncRNAs were identified; 67 of these were included in the ceRNA network. Functional analyses revealed that these 67 key lncRNAs primarily dominated cellular biological processes. The present study then analyzed the associations between the expression levels of these 67 key lncRNAs and the clinicopathological characteristics of the ESCC patients, as well as their survival time using TCGA. The results revealed that 31 of these lncRNAs were associated with tumor grade, tumor‑node‑metastasis (TNM) stage and lymphatic metastasis status (P<0.05). In addition, 15 key lncRNAs were demonstrated to be associated with survival time (P<0.05). Finally, 5 key lncRNAs were selected for validation of their expression levels in 30 patients newly diagnosed with ESCC via reverse transcription‑quantitative PCR (RT‑qPCR). The results suggested that the fold changes in the trends of up‑ and downregulation between GEO, TCGA and RT‑qPCR were consistent. In addition, it was also demonstrated that a select few of these 5 key lncRNAs were significantly associated with TNM stage and lymph node metastasis (P<0.05). The results of the clinically relevant analysis and the aforementioned bioinformatics were similar, hence proving that the bioinformatics analysis used in the present study is credible. Overall, the results from the present study may provide further insight into the functional characteristics of lncRNAs in ESCC through bioinformatics integrative analysis of the GEO and TCGA datasets, and reveal potential diagnostic and prognostic biomarkers for ESCC.

Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

Background

Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening.

Methods

First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA).

Results

Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer.

Conclusion

Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

Reconstruction and analysis of competitive endogenous RNA network reveals regulatory role of long non‑coding RNAs in hepatic fibrosis

Hepatic fibrosis (HF), one of the leading global health problems, is defined as aberrant and excess production of extracellular matrix components. The pathogenesis of HF is complex and poorly understood. Long non‑coding RNAs (LncRNAs) can interact with microRNAs (miRNAs) as competing endogenous RNAs (ceRNAs) to regulate the expression of target genes, which play a significant role in the initiation and progression of HF. In the present study, the LncRNA‑associated ceRNA network was reconstructed based on LncRNA, miRNA and mRNA expression profiles that were downloaded from National Center for Biotechnology Information Gene Expression Omnibus. Bioinformatics assessments including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed with Database for Annotation, Visualization and Integration Discovery. The ceRNA network was composed of 220 LncRNA nodes, 24 miRNA nodes, 164 mRNA nodes and 1,149 edges. Functional assays identified that a total of 338 GO terms and 25 pathways, including regulation of cytokine and collagen, and the transforming growth factor‑β and Toll‑like receptor signaling pathways, were significantly enriched. In addition, 4 LncRNAs (NONMMUT036242, XR_877072, XR_378619 and XR_378418) were highly related to HF and thereby chosen as key LncRNAs. The present study uncovered a ceRNA network that could further the understanding of the mechanisms underlying HF development and provide potential novel markers for clinical diagnosis and targets for treatment.

LncRNA LINC00460 promotes EMT in head and neck squamous cell carcinoma by facilitating peroxiredoxin-1 into the nucleus

BACKGROUND

The lncRNA LINC00460 plays crucial roles in several epithelial cancers, although its mechanisms of action differ greatly in different cellular contexts. In this study, we aimed to determine the potential clinical applications of LINC00460 and elucidate the mechanisms by which LINC00460 affects the development and progression of head and neck squamous cell carcinoma (HNSCC).

METHODS

The biological functions of LINC00460 were assessed in several epithelial cancer cell lines. The subcellular localization of LINC00460 was evaluated by cell nuclear/cytoplasmic fractionation and fluorescence in situ hybridization. RNA pull-down assays, LS-MS/MS analysis, and RNA and chromatin immunoprecipitation assays were performed to identify the molecular mechanism by which LINC00460 promotes HNSCC progression. The clinical pathological features of LINC00460 and PRDX1 were evaluated in HNSCC tissues and paired adjacent normal tissues.

RESULTS

LINC00460 enhanced HNSCC cell proliferation and metastasis in vitro and in vivo and induced epithelial-mesenchymal transition (EMT). LINC00460 primarily localized within the cytoplasm of HNSCC cells, physically interacted with PRDX1 and facilitated PRDX1 entry into the nucleus. PRDX1 promoted the transcription of LINC00460, forming a positive feedback loop. In addition, PRDX1 also promoted the transcription of EMT-related genes (such as ZEB1, ZEB2 and VIM) through enrichment on gene promoters in the nucleus. LINC00460 effectively induced HNSCC cell EMT in a PRDX1-dependent manner, and PRDX1 mainly mediated the EMT-promoting effect of LINC00460. High levels of LINC00460 and PRDX1 expression were positively associated with lymph metastasis, pathological differentiation and tumor size in HNSCC patients.

CONCLUSIONS

LINC00460 promoted EMT in HNSCC cells by facilitating PRDX1 entry into the nucleus. LINC00460 and PRDX1 are promising candidate prognostic predictors and potential targets for cancer therapy for HNSCC.

Identification of a multidimensional transcriptome prognostic signature for lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the leading contributors to cancer-related deaths worldwide. The objective of the current study is to identify a multidimensional transcriptome prognostic signature by combining protein-coding gene (PCG) with long non-coding RNA (lncRNA) for patients with LUAD.

METHODS

We obtained LUAD PCG and lncRNA expression profile data from three datasets in the Gene Expression Omnibus database and conducted survival analyzes for these individuals.

RESULTS

We established a predictive model comprising the three PCGs (NHLRC2, PLIN5, GNAI3), and one lncRNA (AC087521.1). This model segregated patients with LUAD into low- and high-risk groups based on significant differences in survival in the training dataset (GSE31210, n = 226, log-rank test P < .001). Risk stratification of the model was subsequently validated in other two test datasets (GSE37745, n = 106, log-rank test P < .001; GSE30219, n = 85, log-rank test P = .006). Time-dependent receiver operating characteristic (timeROC) curve analysis demonstrated that the model correlated strongly with disease progression and outperformed pathological stage in terms of prognostic ability. Cox proportional hazards regression analysis revealed that the signature could serve as an independent predictor of clinical outcomes in patients with LUAD.

CONCLUSIONS

We describe a novel multidimensional transcriptome signature that can predict survival probabilities in patients with LUAD.

LINC00460 promotes hepatocellular carcinoma development through sponging miR-485-5p to up-regulate PAK1

LncRNAs can function as significant regulators of tumor development. However, their roles in hepatocellular carcinoma (HCC) remain poorly investigated. LINC00460 has been identified in several cancers, which can act as an oncogene. In this study, we observed that LINC00460 was significantly up-regulated in HCC cells, which implied that LINC00460 was involved in HCC development. Then, LINC00460 was silenced in Hep3B and Huh-7 cells and we found that knockdown of LINC00460 greatly inhibited HCC cell proliferation. In addition, HCC cell apoptosis was induced and meanwhile, cell cycle progression was blocked by down-regulation of LINC00460 in vitro. Furthermore, we proved that Hep3B and Huh-7 cell migration and invasion capacity was repressed by decrease of LINC00460. Recently, a growing number of studies have indicated the correlation between lncRNAs and microRNAs. Currently, we displayed that miR-485-5p was greatly decreased in HCC cells and LINC00460 could sponge miR-485-5p to regulate HCC progression. The binding association between LINC00460 and miR-485-5p was confirmed using dual-luciferase reporter assay, RNA pulled down and RIP assay in our research. Subsequently, PAK1 was predicted as a downstream target of miR-485-5p and we demonstrated that miR-485-5p suppressed PAK1 levels in vitro. Finally, in vivo experiments were conducted to validate that knockdown of LINC00460 repressed HCC development through modulating miR-485-5p to increase PAK1. Taken these together, we indicated that LINC00460 promoted HCC progression through sponging miR-485-5p and up-regulating PAK1.

Nicotine promotes the development of non-small cell lung cancer through activating LINC00460 and PI3K/Akt signaling

Objective: Nicotine, the main ingredient in tobacco, is identified to facilitate tumorigenesis and accelerate metastasis in tumor. Studies in recent years have reported that long intergenic non-protein coding RNA 460 (LINC00460) is strongly associated with lung cancer poor prognosis and nicotine dependence. Nonetheless, it is unclear whether nicotine promotes the development of lung cancer through activation of LINC00460. Methods: We determined that LINC00460 expression in lung cancer tissues and the prognosis in patients with non-small cell lung carcinoma (NSCLC) using Gene Expression Profiling Interactive Analysis (GEPIA) website and The Cancer Genome Atlas (TCGA) database. Through in vitro experiments, we studied the effects of nicotine on LINC00460 in NSCLC cells lines using Cell Counting Kit-8 (CCK-8), transwell test, flow cytometry, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and Western blot assays. Results: We identified the significant up-regulated expression level of LINC00460 in NSCLC tissues and cell lines, especially, the negative correlation of LINC00460 expression level with overall survival (OS). In in vitro experiments, LINC00460 was overexpressed in NSCLC cell lines under nicotine stimulation. Nicotine could relieve the effect of LINC00460 knockdown on NSCLC cell proliferation, migration and apoptosis. The same influence was observed on PI3K/Akt signaling pathway. Conclusions: In summary, this is the first time to examine the potential roles of LINC00460 in lung cancer cell proliferation, migration and apoptosis induced by nicotine. This may help to develop novel therapeutic strategies for the prevention and treatment of metastatic tumors from cigarette smoke-caused lung cancer by blocking the nicotine-activated LINC00460 pathway.

Long non-coding RNA (lncRNA) transcriptional landscape in breast cancer identifies LINC01614 as non-favorable prognostic biomarker regulated by TGFβ and focal adhesion kinase (FAK) signaling

Long non-coding RNAs (lncRNAs) represent a class of epigenetic regulators implicated in a number of physiological and pathological conditions. Herein, we characterized the lncRNA expression portrait from 837 patients with invasive breast cancer and 105 normals from the cancer genome atlas (TCGA), which revealed eighteen upregulated and forty-six downregulated lncRNAs. Clustering analysis revealed distinct lncRNA profile for the triple negative breast cancer (TNBC) and normal breast tissue, while less separation was observed among the HER2⁺HR⁺, HER2⁺HR⁻, HER2⁻HR⁺ molecular subtypes. LINC01614, and LINC01235 correlated with worse disease-free survival (DFS), while the expression of lnc-LRR1–1, lnc-ODF3B-2, AC015712.5, lnc-LAMB3–1, lnc-SPP2–3, and lnc-MAP9–2 correlated with better DFS. The expression of LINC01235 correlated with worse overall survival (OS), while the expression of MIR205HG, lnc-MAP2K6–5, FGF14-AS2, lnc-SPP2–3 correlated with better OS. Highest expression of LINC01614 was observed in progesterone receptor (PR)⁺, Estrogen receptor (PR)⁺, and HER2⁺ tumors, while lowest expression was in TNBC. Concordantly, LINC01614 was highly expressed in the luminalB/HER2⁺ subtype from the SRP062132 dataset. Elevated expression of LINC01614 was subsequently validated in primary breast cancer tissue and breast cancer cell lines. Bioinformatics and pathway analyses on LINC01614^high vs. LINC01614^low BC tissue revealed TGFβ1 and ECM as the most activated networks in LINC01614^high tumors. Concordantly, strong correlation between the expression of LINC01614 and COL10A1 (R² = 0.6929), SPOCK1 (R² = 0.5156), ZEB1 (R² = 0.3372), TGFBI (R² = 0.2978), TGFB1 (R² = 0.1985), ACTA2 (R² = 0.1833), and TAGLN (R² = 0.1909) was observed. Mechanistically, exogenous TGFB1 induced LINC01614 expression in the BT474 triple positive BC model, while small-molecule inhibition of transforming growth factor β (TGFβ, SB-431542) or focal adhesion kinase (FAK, PF-573228) abrogated LINC01614 expression. Our data revealed the lncRNA transcription landscape in breast cancer and its molecular subtypes. Our data provide novel insight implicating LINC01614 as unfavorable prognostic marker in BC, its association with the HR⁺/HER2⁺ BC molecular subtype and its regulation by TGFβ and FAK signaling.

An 11-lncRNA expression could be potential prognostic biomarkers in head and neck squamous cell carcinoma

The aim of our study is to construct the competing endogenous RNA (ceRNA) network of head and neck squamous cell carcinoma (HNSCC) and identify key long noncoding RNAs (lncRNAs) to predict prognosis. The genes whose expression were differentially in HNSCC and normal tissues were explored by the Cancer Genome Atlas database. The ceRNA network was constructed by the Cytoscape software. The lncRNAs which could estimate the overall survival were explored from Cox proportional hazards regression. There are 1997, 589, and 82 mRNAs, lncRNAs, and miRNAs whose expression were statistically significant different, respectively. Then, the network between miRNA and mRNA or miRNA and lncRNA was constructed by miRcode, miRDB, TargetScan, and miRanda. Five mRNAs, 10 lncRNAs, and 3 miRNAs were associated with overall survival. Then, 11-lncRNAs were found to be prognostic factors. Therefore, our research analyzed the potential signature of novel 11-lncRNA as candidate prognostic biomarker from the ceRNA network for patients with HNSCC.

HOTTIP Functions as a Key Candidate Biomarker in Head and Neck Squamous Cell Carcinoma by Integrated Bioinformatic Analysis

Background

Accumulating evidence has demonstrated the pivotal role of long noncoding RNAs (lncRNAs) in competing endogenous RNA (ceRNA) networks for predicting survival and evaluating prognosis in cancer patients. However, the pathogenesis of head and neck squamous cell carcinoma (HNSCC) remains unclear, and prognostic biomarkers for HNSCC are still lacking.

Methods

A total of 546 RNA sequencing profiles of HNSCC patients with clinical outcome data were obtained from the Cancer Genome Atlas (TCGA) database, providing a large sample of RNA sequencing data. From these, 71 Long noncoding RNAs lncRNAs, 8 microRNAs (miRNAs), and 16 messenger RNAs (mRNAs) were identified to construct a HNSCC-specific ceRNA network (fold change >2, P < 0.05). Univariate and multivariate Cox proportional regression models were used to assess independent indicators of prognosis. Then the expression of lncRNAs harboring prognostic value was validated in human HNSCC cell lines and tumor samples from our cohort and another two datasets from GEO (Gene Expression Omnibus) databases.

Results

As a result, a 3-mRNA signature and 6-lncRNA signature were identified. The six-lncRNA signature exhibited the highest prognostic value. Notably, in the six lncRNAs, HOTTIP showed the greatest prognostic value and was significantly correlated with clinical stage and histological grade of HNSCC patients. Furthermore, it was proved that HOTTIP was upregulated in HNSCC cell lines and cancerous tissues compared with corresponding normal cell lines and normal tissues. Functional assessment analysis revealed that HOTTIP might play a key role in the oncogenesis and progression of HNSCC.

Conclusion

The present study deepened our understanding of the ceRNA-related regulatory mechanism in the pathogenesis of HNSCC and identified candidate prognostic biomarkers for clinical outcome prediction in HNSCC. HOTTIP may function as a key candidate biomarker in HNSCC and serve as a prognostic marker for HNSCC patients.

Overexpression of long noncoding RNA LINC01419 in esophageal squamous cell carcinoma and its relation to the sensitivity to 5-fluorouracil by mediating GSTP1 methylation

BACKGROUND

Genome-wide sequencing investigations have identified numerous long noncoding RNAs (lncRNAs) among mammals, many of which exhibit aberrant expression in cancers, including esophageal squamous cell carcinoma (ESCC). Herein, this study elucidates the role and mechanism by which LINC01419 regulates the DNA methylation of glutathione S-transferase pi 1 (GSTP1) in relation to ESCC progression and the sensitivity of ESCC cells to 5-fluorouracil (5-FU).

METHODS

LINC01419 and GSTP1 levels were quantified among 38 paired ESCC and adjacent tissue samples collected from patients with ESCC. To ascertain the contributory role of LINC01419 in the progression of ESCC and identify the interaction between LINC01419 and GSTP1 promoter methylation, LINC01419 was overexpressed or silenced, and the DNA methyltransferase inhibitor 5-Aza-CdR was treated.

RESULTS

Data from the GEO database (GSE21362) and the Cancer Genome Atlas displayed elevated levels of LINC01419 and downregulated levels of GSTP1 in the ESCC tissues and cells. The silencing of LINC01419 led to decreased proliferation, increased apoptosis, and enhanced sensitivity to 5-FU in ESCC cells. Notably, LINC01419 could bind to the promoter region of the GSTP1 gene, resulting in elevated GSTP1 methylation and reduced GSTP1 levels via the recruitment of DNA methyltransferase among ESCC cells, whereby ESCC progression was stimulated accompanied by reduced ESCC cell sensitivity to 5-FU. GSTP1 demethylation by 5-Aza-CdR was observed to reverse the effects of LINC01419 overexpression in ESCC cells and the response to 5-FU.

CONCLUSION

Highly expressed LINC01419 in ESCC promotes GSTP1 methylation, which ultimately acts to promote the event of ESCC and diminish the sensitivity of ESCC cells to 5-FU, highlighting a novel potential strategy to improve 5-FU-based chemotherapy in ESCC.

The construction and analysis of ceRNA networks in invasive breast cancer: a study based on The Cancer Genome Atlas

Background

Studies have shown that long noncoding RNAs (lncRNAs) make up the major proportion of the ceRNA network and can regulate gene expression by competitively binding to miRNAs. This reveals the existence of an RNA-miRNA regulatory pathway and is of great biological significance. CeRNAs, as competitive endogenous RNAs, have revealed a new mechanism of interaction between RNAs. Until now, the role of lncRNA-mediated ceRNAs in breast cancer and their regulatory mechanisms have been elucidated to some extent.

Purpose

In this study, comprehensive analysis of large-scale invasive breast cancer samples in TCGA were conducted to further explore the developmental mechanism of invasive breast cancer and the potential predictive markers for invasive breast cancer prognosis in the ceRNA network.

Methods

Abnormal expression profiles of invasive breast cancer associated mRNAs, lncRNAs and miRNAs were obtained from the TCGA database. Through further alignment and prediction of target genes, an abnormal lncRNA-miRNA-mRNA ceRNA network was constructed for invasive breast cancer. Through the overall survival analysis, Identification prognostic bio-markers for invasive breast cancer patients. In addition, we used Cytoscape plug-in BinGo for the different mRNA performance functional cluster analysis.

Results

Differential analysis revealed that 1059 lncRNAs, 86 miRNAs, and 2138 mRNAs were significantly different in invasive breast cancer samples versus normal samples. Then we construct an abnormal lncRNA-miRNA-mRNA ceRNA network for invasive breast cancer, consisting of 90 DElncRNAs, 18 DEmiRNAs and 26 DEmRNAs.Further, 4 out of 90 lncRNAs, 3 out of 26 mRNAs, and 2 out of 18 miRNAs were useful as prognostic biomarkers for invasive breast cancer patients (P value < 0.05). It is worth noting that based on the ceRNA network, we found that the LINC00466-Hsa-mir-204- NTRK2 LINC00466-hsa-mir-204-NTRK2 axis was present in 9 RNAs associated with the prognosis of invasive breast cancer.

Conclusion

This study provides an effective bioinformatics basis for further understanding of the molecular mechanism of invasive breast cancerand for predicting outcomes, which can guide the use of invasive breast cancerdrugs and subsequent related research.

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (-0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.

Integrated analysis of long noncoding RNA associated-competing endogenous RNA as prognostic biomarkers in clear cell renal carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant carcinomas and its molecular mechanisms remain unclear. Long noncoding RNA (lncRNA) could bind sites of miRNA which affect the expression of mRNA according to the competing endogenous (ceRNA) theory. The aim of the present study was to construct a ceRNA network and to identify key lncRNA to predict survival prognosis. We identified differentially expressed mRNA, lncRNA and miRNA between tumor tissues and normal tissues from The Cancer Genome Atlas database. Then, using bioinformatics tools, we explored the connection of 89 lncRNA, 10 miRNA and 22 mRNA, and we constructed the ceRNA network. Furthermore, we analyzed the functions and pathways of 22 differentially expressed mRNA. Then, univariate and multivariate Cox regression analyses of these 89 lncRNA and overall survival were explored. Nine lncRNA were finally screened out in the training group. The patients were divided into high‐risk and low‐risk groups according to the 9 lncRNA and low‐risk scores having better clinical overall survival (P < .01). Furthermore, the receiver operating characteristic curve demonstrates the predicted role of the 9 lncRNA. The 9‐lncRNA signature was successfully proved in the testing group and the entire group. Finally, multivariate Cox regression analysis and stratification analysis further proved that the 9‐lncRNA signature was an independent factor to predict survival. In summary, the present study provides a deeper understanding of the lncRNA‐related ceRNA network in ccRCC and suggests that the 9‐lncRNA signature could serve as an independent biomarker to predict survival in ccRCC patients.