Construction of lncRNA-mediated ceRNA network to reveal clinically relevant lncRNA biomarkers in glioblastomas

A novel approach to the analysis of Overall Survival (OS) as response with Progression-Free Interval (PFI) as condition based on the RNA-seq expression data in The Cancer Genome Atlas (TCGA)

BACKGROUND

Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS.

RESULTS

Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI.

CONCLUSIONS

Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.

EPB41L4A-AS1 is required to maintain basal autophagy to modulates Aβ clearance

Alzheimer's disease (AD) is the most common neurodegenerative disorder characterized by the deposition of β-amyloid (Aβ) plaques. Aβ is generated from the cleavage of the amyloid precursor protein by β and γ-secretases and cleared by neuroglial cells mediated autophagy. The imbalance of the intracellular Aβ generation and clearance is the causative factor for AD pathogenesis. However, the exact underlying molecular mechanisms remain unclear. Our previous study reported that EPB41L4A-AS1 is an aging-related long non-coding RNA (lncRNA) that is repressed in patients with AD. In this study, we found that downregulated EPB41L4A-AS1 in AD inhibited neuroglial cells mediated-Aβ clearance by decreasing the expression levels of multiple autophagy-related genes. We found that EPB41L4A-AS1 regulates the expression of general control of amino acid synthesis 5-like 2, an important histone acetyltransferase, thus affecting histone acetylation, crotonylation, and lactylation near the transcription start site of autophagy-related genes, ultimately influencing their transcription. Collectively, this study reveals EPB41L4A-AS1 as an AD-related lncRNA via mediating Aβ clearance and provides insights into the epigenetic regulatory mechanism of EPB41L4A-AS1 in gene expression and AD pathogenesis.

Comprehensive analysis of cuproptosis-associated LncRNAs predictive value and related CeRNA network in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is characterized by a high recurrence and mortality rate. Cuproptosis is involved in cell death regulation in in a variety of solid tumors. Long non-coding RNAs that regulate cuproptosis genes in the pathogenesis of acute leukemia have yet to be explored.

Methods

First, cuproptosis genes with distinct expression levels were discovered by contrasting AML with normal samples from the TCGA and GTEx cohorts. Pearson correlation and univariate Cox-regression analysis were performed to identify cuproptosis-associated lncRNAs with significant prognostic values. Then the least absolute shrinkage and selection operator (LASSO) Cox regression was utilized to establish a multi-gene signature to predict AML prognosis. Next, Kaplan-Meier estimator, receiver operating characteristic curve, and a nomogram were performed to evaluate the predictive capacity of the risk signature. Functional enrichment analyses were employed to assess their function. Moreover, qRT-PCR testing of lncRNA expression in AML samples was conducted. The competing endogenous RNA (ceRNA) network was constructed to find the target genes.

Results

A risk model based on the signature of three cuproptosis-associated lncRNAs was developed. The results showed that the model possessed excellent prognostic potential. The nomogram raised the accuracy in predicting AML survival. In addition, functional enrichment analyses demonstrated an enrichment of inflammatory and immune-related pathways. Moreover, correlations between the risk signature and clinicopathological variables, tumor mutational burden, RNA stemness score, immune profile, and drug sensitivity were observed. Furthermore, we discovered that TRAF3IP2-AS1 may function as a ceRNA to regulate cuproptosis and ferroptosis gene expression.

Conclusion

The risk signature established in this study could serve as a reliable biosignature for AML prognosis. And the findings presented here may facilitate research on cuproptosis in AML.

IncRNA EPB41L4A-AS1 Mitigates the Proliferation of Non-Small-Cell Lung Cancer Cells through the miR-105-5p/GIMAP6 Axis

Non-small-cell lung cancer (NSCLC) is the major subtype of lung cancer, with a series of long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and proteins involved in its pathogenesis. This study sought to investigate the functionality of lncRNA EPB41L4A antisense RNA 1 (lncRNA EPB41L4A-AS1) in the proliferation of NSCLC cells and provide a novel theoretical reference for NSCLC treatment. Levels of lncRNA EPB41L4A-AS1, miR-105-5p, and GTPase, IMAP family member 6 (GIMAP6) in tissues and cells were measured by RT-qPCR and the correlation between lncRNA EPB41L4A-AS1 and clinicopathological characteristics was analyzed. Cell proliferation was evaluated by cell counting kit-8 and colony formation assays. The subcellular localization of lncRNA EPB41L4A-AS1 was analyzed by the subcellular fractionation assay and the binding of miR-105-5p to lncRNA EPB41L4A-AS1 or GIMAP6 was analyzed by dual-luciferase and RNA pull-down assays. Functional rescue experiments were performed to analyze the role of miR-105-5p/GIMAP6 in NSCLC cell proliferation. lncRNA EPB41L4A-AS1 and GIMAP6 were downregulated while miR-105-5p was upregulated in NSCLC tissues and cells. lncRNA EPB41L4A-AS1 was correlated with tumor size and clinical staging and its overexpression reduced NSCLC cell proliferation. lncRNA EPB41L4A-AS1 was negatively correlated with miR-105-5p and positively correlated with GIMAP6 in NSCLC tissues, and lncRNA EPB41L4A-AS1 sponged miR-105-5p to promote GIMAP6 transcription in NSCLC cells. Overexpression of miR-105-5p or knockdown of GIMAP6 reversed the inhibition of lncRNA EPB41L4A-AS1 overexpression on NSCLC cell proliferation. lncRNA EPB41L4A-AS1 was downregulated in NSCLC and mitigated NSCLC cell proliferation through the miR-105-5p/GI-MAP6 axis.

Integrated Analysis of the lncRNA-Associated ceRNA Network in Wilms Tumor via TARGET and GEO Databases

Wilms tumor (WT) is the most common genitourinary renal tumor that typically occurs in children under 15 and is thought to be linked to somatic and germline mutations. However, the specific functional role of competing endogenous RNAs (ceRNAs) and their potential implications in WT remain unclear. In this study, we developed an lncRNA-mediated (long noncoding RNA-mediated) ceRNA network via the R packages for WT with expression data obtained from the tumor alterations relevant for genomics-driven therapy (TARGET) database. Unsupervised hierarchical clustering analysis revealed that the WT specimens could be clearly distinguished from healthy specimens with respect to the expression of disordered RNAs. A total of 1,607 differentially expressed (DE) lncRNAs, 116 DE microRNAs (DEmiRNAs), and 3,262 DE messenger RNAs (DEmRNAs) were identified as WT-specific RNAs, and a lncRNA-miRNA-mRNA ceRNA network with 159 DElncRNAs, 18 DEmiRNAs, 131 DEmRNAs, and 792 interactions was constructed. According to the clinical survival data, 12 DElncRNAs, 5 DEmRNAs, and 2 DEmiRNAs were selected from the ceRNA network that could significantly impact the overall survival of WT patients (P < 0.05). Functional enrichment analysis showed that the biological processes and pathways of DEmRNAs, such as cell cycle and virus infection, may be associated with WT. The present study constructed a dysregulated lncRNA-mediated ceRNA network in WT and discovered that lncRNA-mediated ceRNAs may serve as important regulators in WT development and progression. Survival-associated RNAs may serve as new potential biomarkers, suggesting that the constructed ceRNA network in WT might be important for determining optimal therapeutic strategies.

Signature of m5C-Related lncRNA for Prognostic Prediction and Immune Responses in Pancreatic Cancer

BACKGROUND

Pancreatic cancer (PC) has a high mortality and dismal prognosis, predicting to be the second most lethal malignancy. 5-Methylcytosine (m5C) and long noncoding RNAs (lncRNAs) are both crucial in the prognostic outcome and immunotherapeutic effect for PC patients. Therefore, we aimed to create an m5C-related lncRNA signature (m5C-LS) for PC patients' prognosis and treatment.

METHODS

Clinicopathological information and RNAseq data were acquired from The Cancer Genome Atlas (TCGA) database. Pearson's correlation analysis was used to extract m5C-related lncRNAs in PC. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were adopted to build an m5C-LS. Kaplan-Meier (K-M), principal component analysis (PCA), and nomogram were utilized to assess model accuracy. In addition, we explored the model's possible immunotherapeutic responses and drug sensitivity targets.

RESULTS

Three m5C-related lncRNAs were finally established to construct the risk signature, which has a good and independent predictive ability for PC patients. Based on the m5C-LS, patients were classified into the low- and high-m5C-LS group, with the latter having a worse prognosis. Furthermore, the m5C-LS allowed us to better discriminate the immunotherapeutic responses of PC patients in different subgroups.

CONCLUSIONS

Our study constructed an m5C-LS and established a nomogram model that accurately predicted the prognosis of PC patients, as well as provides promising immunotherapeutic strategies in the future.

Prognostication of Pancreatic Cancer Using The Cancer Genome Atlas Based Ferroptosis-Related Long Non-Coding RNAs

Background: Long non-coding RNAs (lncRNAs) are key regulators of pancreatic cancer development and are involved in ferroptosis regulation. LncRNA transcript levels serve as a prognostic factor for pancreatic cancer. Therefore, identifying ferroptosis-related lncRNAs (FRLs) with prognostic value in pancreatic cancer is critical.

Methods: In this study, FRLs were identified by combining The Cancer Genome Atlas (TCGA) and FerrDb databases. For training cohort, univariate Cox, Lasso, and multivariate Cox regression analyses were applied to identify prognosis FRLs and then construct a prognostic FRLs signature. Testing cohort and entire cohort were applied to validate the prognostic signature. Moreover, the nomogram was performed to predict prognosis at different clinicopathological stages and risk scores. A co-expression network with 76 lncRNA-mRNA targets was constructed.

Results: Univariate Cox analysis was performed to analyze the prognostic value of 193 lncRNAs. Furthermore, the least absolute shrinkage and selection operator and the multivariate Cox analysis were used to assess the prognostic value of these ferroptosis-related lncRNAs. A prognostic risk model, of six lncRNAs, including LINC01705, AC068620.2, TRAF3IP2-AS1, AC092171.2, AC099850.3, and MIR193BHG was constructed. The Kaplan Meier (KM) and time-related receiver operating characteristic (ROC) curve analysis were performed to calculate overall survival and compare high- and low-risk groups. There was also a significant difference in survival time between the high-risk and low-risk groups for the testing cohort and the entire cohort, with AUCs of .723, .753, respectively. Combined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for pancreatic adenocarcinoma through univariate and multivariate Cox regression. Moreover, a nomogram showed good prediction.

Conclusion: The signature of six FRLs had significant prognostic value for pancreatic adenocarcinoma. They may be a promising therapeutic target in clinical practice.

m6A-related lncRNAs predict prognosis and indicate immune microenvironment in acute myeloid leukemia

Acute myeloid leukemia (AML) is a complex hematologic malignancy. Survival rate of AML patients is low. N6-methyladenosine (m⁶A) and long non-coding RNAs (lncRNAs) play important roles in AML tumorigenesis and progression. However, the relationship between lncRNAs and biological characteristics of AML, as well as how lncRNAs influence the prognosis of AML patients, remain unclear. In this study. In this study, Pearson correlation analysis was used to identify lncRNAs related to m⁶A regulatory genes, namely m⁶A-related lncRNAs. And we analyzed their roles and prognostic values in AML. m⁶A-related lncRNAs associated with patient prognosis were screened using univariate Cox regression analysis, followed by systematic analysis of the relationship between these genes and AML clinicopathologic and biologic characteristics. Furthermore, we examined the characteristics of tumor immune microenvironment (TIME) using different IncRNA clustering models. Using LASSO regression, we identified the risk signals related to prognosis of AML patients. We then constructed and verified a risk model based on m⁶A-related lncRNAs for independent prediction of overall survival in AML patients. Our results indicate that risk scores, calculated based on risk-related signaling, were related to the clinicopathologic characteristics of AML and level of immune infiltration. Finally, we examined the expression level of TRAF3IP2-AS1 in patient samples through real-time polymerase chain reaction analysis and in GEO datasets, and we identified a interaction relationship between SRSF10 and TRAF3IP2-AS1 through in vitro assays. Our study shows that m⁶A-related lncRNAs, evaluated using the risk prediction model, can potentially be used to predict prognosis and design immunotherapy in AML patients.

N6-Methyladenosine-Related Long Noncoding RNAs as Potential Prognosis Biomarkers for Endometrial Cancer

Purpose

Endometrial cancer (EC) is a common gynaecologic malignancy with an increasing incidence rate and mortality in recent years. N6-methylandenosine (m6A)-related long noncoding RNA (lncRNA) plays a vital role in EC, emerging as one of the most abundant RNA modifications.

Materials and Methods

The Cancer Genome Atlas (TCGA) database and UCSC Xena were used to download data related to EC. Survival and univariate and multifactorial prognostic analyses were performed for m6A-related lncRNAs. The expression levels of the three lncRNAs were verified using q-PCR. A nomogram was used to create a clinical tool to assess overall survival. To investigate the relationship between m6A-related lncRNA and EC, we downloaded differential genes related to EC from the TCGA database and mined three m6A-related lncRNAs, namely SCARNA9, TRAF3IP2-AS1, and AL133243.2. The data were categorized into high- and low-risk groups based on m6A-associated lncRNA.

Results

Survival analysis revealed that the high-risk group had a lower survival rate. Survival analysis of three m6A-associated lncRNAs revealed that cases with high expression of SCARNA9 tended to have a poorer prognosis, whereas the opposite was true for TRAF3IP2-AS1, AL133243.2. Univariate and multifactorial prognostic analyses suggested statistical differences in patients’ age, FIGO stage, pathological grade, risk score, and prognosis of EC, which was confirmed by results of the separate prognostic factor analysis for the three lncRNAs. Risk status was validated as an independent prognostic indicator, and the prognostic nomogram combined patient age, pathological stage, and FIGO classification to assess 3–5-year survival. Cases from high- and low-risk groups were analysed for the tumour microenvironment and immune cell scores, and stromal cell scores were found to be lower in the high-risk group. Correlations were analysed using different databases for immune cell classification.

Conclusion

m6A-related lncRNAs may play a key role in the diagnosis and treatment of EC as targets of prognosis and the immune microenvironment.

Construction and comprehensive analysis of a competitive endogenous RNA network to reveal potential biomarkers for the malignant differentiation of glioma

BACKGROUND

Long noncoding RNAs (lncRNAs) can act as microRNA (miRNA) sponges to regulate protein-coding gene expression; therefore, lncRNAs are considered major components of the competitive endogenous RNA (ceRNA) network and have attracted growing attention. This study explored the regulatory mechanisms and functional roles of lncRNAs as ceRNAs in the malignant differentiation of low-grade glioma (LGG) to glioblastoma (GBM) and their potential impact on the prognosis of patients with GBM.

METHODS

LncRNA and messenger RNA (mRNA) data were extracted from the Cancer Genome Atlas (TCGA) database from 156 GBM samples and 529 LGG samples. Separately, the miRNA expression data were downloaded from the Gene Expression Omnibus database, with the GSE112009 dataset containing miRNA expression data from 10 GBM samples and 15 LGG samples. Weighted gene coexpression network analysis was performed to screen the glioma grade-related lncRNAs. Then, a ceRNA network was established. The database for annotation, visualization, and integrated discovery was adopted to conduct functional enrichment analysis based on 57 upregulated differentially expressed mRNAs in the ceRNA network. Finally, Kaplan-Meier curves were created for the survival analysis of 13 hub lncRNA by combining the clinical data of GBM patients in TCGA.

RESULTS

A ceRNA network including 16 lncRNAs, 18 miRNAs, and 78 mRNAs specific to the malignant differentiation of LGG to GBM was established. The 57 upregulated differentially expressed mRNAs in the ceRNA network were significantly enriched in 35 gene ontology terms and 5 pathways. The survival analysis showed that 2 lncRNAs (LINC00261 and HOXA10-AS) were prognostic biomarkers for patients with GBM in TCGA.

CONCLUSION

The proposed ceRNA network may help elucidate the regulatory mechanism by which lncRNAs function as ceRNAs and contribute to the malignant differentiation of LGG to GBM. Importantly, the candidate lncRNAs, miRNAs, and mRNAs involved in the ceRNA network can be further evaluated as potential therapeutic targets and prognostic biomarkers for GBM.

lncRNA TPT1‑AS1 knockdown inhibits liver cancer cell proliferation, migration and invasion

Long non-coding RNA (lncRNA) tumor protein translationally controlled 1 antisense RNA 1 (TPT1-AS1) serves as an oncogene in several tumors, including ovarian and cervical cancer. However, the functional role of TPT1-AS1 in liver cancer (LC) is not completely understood. The present study aimed to explore the role of TPT1-AS1 in LC. In this study, the reverse transcription-quantitative PCR results demonstrated that TPT1-AS1 expression was significantly upregulated in LC tissues and cell lines compared with adjacent paracancerous tissues and THLE-3 cells, respectively. Elevated TPT1-AS1 expression was significantly associated with TNM stage lymph node metastasis and poor prognosis in patients with LC, as determined via χ² and Kaplan-Meier survival analyses. By constructing TPT1-AS1 knockdown LC cell lines (HepG2 and SNU-182), loss-of-function experiments, including Cell Counting Kit-8, colony formation, flow cytometry, wound healing and Transwell assays, were performed to explore the function role of TPT1-AS1 in LC in vitro. The results demonstrated that TPT1-AS1 knockdown inhibited LC cell proliferation, G₁/S transition, migration and invasion compared with the small interfering RNA (si)-negative control (NC) group. Mechanistically, TPT1-AS1 knockdown markedly decreased CDK4, N-cadherin and Vimentin expression levels, but notably increased p21 and E-cadherin expression levels compared with the si-NC group. Therefore, the results of the present study suggested that TPT1-AS1 might serve as a promising therapeutic target for LC treatment.

The long non-coding RNA βFaar regulates islet β-cell function and survival during obesity in mice

Despite obesity being a predisposing factor for pancreatic β-cell dysfunction and loss, the mechanisms underlying its negative effect on insulin-secreting cells remain poorly understood. In this study, we identify an islet-enriched long non-coding RNA (lncRNA), which we name β-cell function and apoptosis regulator (βFaar). βFaar is dramatically downregulated in the islets of the obese mice, and a low level of βFaar is necessary for the development of obesity-associated β-cell dysfunction and apoptosis. Mechanistically, βFaar promote the synthesis and secretion of insulin by upregulating islet-specific genes Ins2, NeuroD1, and Creb1 through sponging miR-138-5p. In addition, using quantitative mass spectrometry, we identify TRAF3IP2 and SMURF1 as interacting proteins that are specifically associated with βFaar. We demonstrate that SMURF1 ubiquitin ligase activity is essential for TRAF3IP2 ubiquitination and activation of NF-κB-mediate β-cell apoptosis. Our experiments provide direct evidence that dysregulated βFaar contributes to the development of obesity-induced β-cell injury and apoptosis.

Beta-cell function is often impaired in obesity through incompletely understood mechanisms. Here the authors show that the long noncoding RNA βFaar is reduced by diet-induced obesity in mice, which leads to impaired beta-cell function via miR-138-5p and survival via TRAF3 Interacting Protein 2.

Low expression of TRAF3IP2-AS1 promotes progression of NONO-TFE3 translocation renal cell carcinoma by stimulating N6-methyladenosine of PARP1 mRNA and downregulating PTEN

Background

NONO-TFE3 translocation renal cell carcinoma (NONO-TFE3 tRCC) is one subtype of RCCs associated with Xp11.2 translocation/TFE3 gene fusions RCC (Xp11.2 tRCCs). Long non-coding RNA (lncRNA) has attracted great attention in cancer research. The function and mechanisms of TRAF3IP2 antisense RNA 1 (TRAF3IP2-AS1), a natural antisense lncRNA, in NONO-TFE3 tRCC remain poorly understood.

Methods

FISH and qRT-PCR were undertaken to study the expression, localization and clinical significance of TRAF3IP2-AS1 in Xp11.2 tRCC tissues and cells. The functions of TRAF3IP2-AS1 in tRCC were investigated by proliferation analysis, EdU staining, colony and sphere formation assay, Transwell assay and apoptosis analysis. The regulatory mechanisms among TRAF3IP2-AS1, PARP1, PTEN and miR-200a-3p/153-3p/141-3p were investigated by luciferase assay, RNA immunoprecipitation, Western blot and immunohistochemistry.

Results

The expression of TRAF3IP2-AS1 was suppressed by NONO-TFE3 fusion in NONO-TFE3 tRCC tissues and cells. Overexpression of TRAF3IP2-AS1 inhibited the proliferation, migration and invasion of UOK109 cells which were derived from cancer tissue of patient with NONO-TFE3 tRCC. Mechanistic studies revealed that TRAF3IP2-AS1 accelerated the decay of PARP1 mRNA by direct binding and recruitment of N⁶-methyladenosie methyltransferase complex. Meanwhile, TRAF3IP2-AS1 competitively bound to miR-200a-3p/153-3p/141-3p and prevented those from decreasing the level of PTEN.

Conclusions

TRAF3IP2-AS1 functions as a tumor suppressor in NONO-TFE3 tRCC progression and may serve as a novel target for NONO-TFE3 tRCC therapy. TRAF3IP2-AS1 expression has the potential to serve as a novel diagnostic and prognostic biomarker for NONO-TFE3 tRCC detection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13045-021-01059-5.

Integrated Analysis of lncRNA-miRNA-mRNA ceRNA Network Identified lncRNA EPB41L4A-AS1 as a Potential Biomarker in Non-small Cell Lung Cancer

Background

Recent evidence has indicated that long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) to modulate mRNAs expression by sponging microRNAs (miRNAs). However, the specific mechanism and function of lncRNA-miRNA-mRNA regulatory network in non-small cell lung cancer (NSCLC) remains unclear.

Materials and Methods

We constructed a lung cancer related lncRNA-mRNA network (LCLMN) by integrating differentially expressed genes (DEGs) with miRNA-target interactions. We further performed topological feature analysis and random walk with restart (RWR) analysis of LCLMN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to investigate the target DEGs in LCLMN. The expression levels of significant lncRNAs in NSCLC were validated by quantitative real-time PCR (RT-qPCR). The prognostic value of the potential lncRNA was evaluated by Kaplan-Meier analysis.

Results

A total of 33 lncRNA nodes, 580 mRNA nodes and 2105 edges were identified from LCLMN. Based on functional enrichment analysis and co-expression analysis, lncRNA EPB41L4A-AS1 was demonstrated to be correlated with the tumorigenesis of NSCLC. RT-qPCR results confirmed that the expression levels of lncRNA EPB41L4A-AS1 in NSCLC tissues were downregulated compared with adjacent non-cancerous tissues. Kaplan-Meier analysis showed that high expression of lncRNA EPB41L4A-AS1 was associated with better overall survival (OS) in NSCLC patients. Further investigation identified that high expression levels of COL4A3BP, CDS2, PURA, PDCD6IP, and TMEM245 were also correlated with better OS in NSCLC patients.

Conclusion

In this study, we constructed a lncRNA-miRNA-mRNA ceRNA network to investigate potential prognostic biomarkers for NSCLC. We found that lncRNA EPB41L4A-AS1 could function as a regulator in the pathogenesis of NSCLC.

Genome-wide identification of CpG island methylator phenotype related gene signature as a novel prognostic biomarker of gastric cancer

Background

Gastric cancer (GC) is one of the most fatal cancers in the world. Results of previous studies on the association of the CpG island methylator phenotype (CIMP) with GC prognosis are conflicting and mainly based on selected CIMP markers. The current study attempted to comprehensively assess the association between CIMP status and GC survival and to develop a CIMP-related prognostic gene signature of GC.

Methods

We used a hierarchical clustering method based on 2,082 GC-related methylation sites to stratify GC patients from the cancer genome atlas into three different CIMP subgroups according to the CIMP status. Gene set enrichment analysis, tumor-infiltrating immune cells, and DNA somatic mutations analysis were conducted to reveal the genomic characteristics in different CIMP-related patients. Cox regression analysis and the least absolute shrinkage and selection operator were performed to develop a CIMP-related prognostic signature. Analyses involving a time-dependent receiver operating characteristic (ROC) curve and calibration plot were adopted to assess the performance of the prognostic signature.

Results

We found a positive relationship between CIMP and prognosis in GC. Gene set enrichment analysis indicated that cancer-progression-related pathways were enriched in the CIMP-L group. High abundances of CD8+ T cells and M1 macrophages were found in the CIMP-H group, meanwhile more plasma cells, regulatory T cells and CD4+ memory resting T cells were detected in the CIMP-L group. The CIMP-H group showed higher tumor mutation burden, more microsatellite instability-H, less lymph node metastasis, and more somatic mutations favoring survival. We then established a CIMP-related prognostic gene signature comprising six genes (CST6, SLC7A2, RAB3B, IGFBP1, VSTM2L and EVX2). The signature was capable of classifying patients into high‐and low‐risk groups with significant difference in overall survival (OS; p < 0.0001). To assess performance of the prognostic signature, the area under the ROC curve (AUC) for OS was calculated as 0.664 at 1 year, 0.704 at 3 years and 0.667 at 5 years. When compared with previously published gene-based signatures, our CIMP-related signature was comparable or better at predicting prognosis. A multivariate Cox regression analysis indicated the CIMP-related prognostic gene signature was an independent prognostic indicator of GC. In addition, Gene ontology analysis indicated that keratinocyte differentiation and epidermis development were enriched in the high-risk group.

Conclusion

Collectively, we described a positive association between CIMP status and prognosis in GC and proposed a CIMP-related gene signature as a promising prognostic biomarker for GC.

Long non-coding RNA TPT1-AS1 promotes angiogenesis and metastasis of colorectal cancer through TPT1-AS1/NF90/VEGFA signaling pathway

LncRNAs have been proven closely correlated to tumor progression. A recent study identified LncRNA TPT1-AS1 (TPT1-AS1) as one of the liver-metastasis associated LncRNAs in colorectal cancer (CRC). In this study, we report that TPT1-AS1 is upregulated in CRC tissues, which is associated with poor prognosis. Functional assays unravel a pro-angiogenesis and metastasis role of TPT1-AS1. Mechanistically, Flexmap 3D assays reveal that TPT1-AS1 upregulates the VEGFA secretion in CRC cells. RNA immunoprecipitation and mRNA stability assays further show that TPT1-AS1 interacts with nuclear factor 90 (NF90) and subsequently promotes the association between NF90 and VEGFA mRNA, which leads to the upregulation of VEGFA mRNA stability. Therefore, we elucidate a new regulatory mechanism of TPT1-AS1 in CRC angiogenesis and targeting the TPT1-AS1/NF90/VEGFA axis may provide a useful strategy for diagnosis and treatment for colorectal cancer patients.

Identification of Cancer-Related Long Non-Coding RNAs Using XGBoost With High Accuracy

In the past decade, hundreds of long noncoding RNAs (lncRNAs) have been identified as significant players in diverse types of cancer; however, the functions and mechanisms of most lncRNAs in cancer remain unclear. Several computational methods have been developed to detect associations between cancer and lncRNAs, yet those approaches have limitations in both sensitivity and specificity. With the goal of improving the prediction accuracy for associations of lncRNA with cancer, we upgraded our previously developed cancer-related lncRNA classifier, CRlncRC, to generate CRlncRC2. CRlncRC2 is an eXtreme Gradient Boosting (XGBoost) machine learning framework, including Synthetic Minority Over-sampling Technique (SMOTE)-based over-sampling, along with Laplacian Score-based feature selection. Ten-fold cross-validation showed that the AUC value of CRlncRC2 for identification of cancer-related lncRNAs is much higher than previously reported by CRlncRC and others. Compared with CRlncRC, the number of features used by CRlncRC2 dropped from 85 to 51. Finally, we identified 439 cancer-related lncRNA candidates using CRlncRC2. To evaluate the accuracy of the predictions, we first consulted the cancer-related long non-coding RNA database Lnc2Cancer v2.0 and relevant literature for supporting information, then conducted statistical analysis of somatic mutations, distance from cancer genes, and differential expression in tumor tissues, using various data sets. The results showed that our approach was highly reliable for identifying cancer-related lncRNA candidates. Notably, the highest ranked candidate, lncRNA AC074117.1, has not been reported previously; however, integrated multi-omics analyses demonstrate that it is the target of multiple cancer-related miRNAs and interacts with adjacent protein-coding genes, suggesting that it may act as a cancer-related competing endogenous RNA, which warrants further investigation. In conclusion, CRlncRC2 is an effective and accurate method for identification of cancer-related lncRNAs, and has potential to contribute to the functional annotation of lncRNAs and guide cancer therapy.

A 3-mRNA-based prognostic signature of survival in oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) is the most common type of head and neck squamous cell carcinoma with an unsatisfactory prognosis. The aim of this study was to identify potential prognostic mRNA biomarkers of OSCC based on analysis of The Cancer Genome Atlas (TCGA).

Methods

Expression profiles and clinical data of OSCC patients were collected from TCGA database. Univariate Cox analysis and the least absolute shrinkage and selection operator Cox (LASSO Cox) regression were used to primarily screen prognostic biomarkers. Then multivariate Cox analysis was performed to build a prognostic model based on the selected prognostic mRNAs. Nomograms were generated to predict the individual’s overall survival at 3 and 5 years. The model performance was assessed by the time-dependent receiver operating characteristic (ROC) curve and calibration plot in both training cohort and validation cohort (GSE41613 from NCBI GEO databases). In addition, machine learning was used to assess the importance of risk factors of OSCC. Finally, in order to explore the potential mechanisms of OSCC, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was completed.

Results

Three mRNAs (CLEC3B, C6 and CLCN1) were finally identified as a prognostic biomarker pattern. The risk score was imputed as: (−0.38602 × expression level of CLEC3B) + (−0.20632 × expression level of CLCN1) + (0.31541 × expression level of C6). In the TCGA training cohort, the area under the curve (AUC) was 0.705 and 0.711 for 3- and 5-year survival, respectively. In the validation cohort, AUC was 0.718 and 0.717 for 3- and 5-year survival. A satisfactory agreement between predictive values and observation values was demonstrated by the calibration curve in the probabilities of 3- and 5- year survival in both cohorts. Furthermore, machine learning identified the 3-mRNA signature as the most important risk factor to survival of OSCC. Neuroactive ligand-receptor interaction was most enriched mostly in KEGG pathway analysis.

Conclusion

A 3-mRNA signature (CLEC3B, C6 and CLCN1) successfully predicted the survival of OSCC patients in both training and test cohort. In addition, this signature was an independent and the most important risk factor of OSCC.