Comprehensive investigation of a novel differentially expressed lncRNA expression profile signature to assess the survival of patients with colorectal adenocarcinoma

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

Background

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

Methods

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

Results

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

Conclusions

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

Integrated Bioinformatics and Experimental Analysis of Long Noncoding RNA Associated-ceRNA as Prognostic Biomarkers in Advanced Stomach Adenocarcinoma

BACKGROUND

Advanced stomach adenocarcinoma (ASTAD) is a highly malignant and prognostically poor stage of gastric cancer. Recently, long noncoding RNA (lncRNA) was found to play a crucial role, including as competing endogenous RNA (ceRNA) in cancer. However, studies on large-scale sample in ASTAD are still lacking, thus we constructed the ceRNA network of ASTAD to explore its molecular mechanism.

METHODS

We compared the expression of mRNAs, lncRNAs and miRNAs between ASTAD and normal tissues utilizing RNA-Seq and miRNA-seq Data from The Cancer Genome Atlas (TCGA). GO and KEGG enrichment analysis were executed for annotating the functions of differentially expressed mRNAs. Subsequently, we investigated the expression correlations between the differentially expressed lncRNAs and their respective mRNAs by constructing a ceRNA network. Kaplan-Meier survival analysis was used to assess the relationship between high/low risk scores based on this network with patient prognosis in TCGA training cohort and GSE15459 validation cohort. In vitro functional assays were employed to verify the cancer-promoting effects of key lncRNAs in the ceRNA network and their possible mechanisms.

RESULTS

In ASTAD tissues, a total of 176 lncRNAs, 124 miRNAs, and 2205 mRNAs were identified as differentially expressed. Our constructed ceRNA network consisted 6 differentially expressed lncRNAs (PVT1, MAGI2-AS3, KCNQ1OT1, LINC02086, AC125807.2 and LINC02535), 25 miRNAs and 130 mRNAs, and the risk score derived from these lincRNAs could predict ASTAD patient outcomes. Key lncRNA LINC02086 was experimentally verified to enhance proliferation and migration of gastric cancer cells by competitively binding to miR-93a-5p with MMP3.

CONCLUSION

Our comprehensive ceRNA network for ASTAD provides valuable insights into its molecular mechanisms, and LINC02086 may be used as an innovative target for clinical treatment.

Genome-wide RNA-sequencing dataset reveals AC096751.1 sever as a novel prognostic long non-coding RNA and its potential molecular mechanisms in patients with colon adenocarcinoma

Objective: Through data analysis, we observed that AC096751.1 is markedly imbalance between colon adenocarcinoma (COAD) cancer and paracancerous tissues. However, the prognostic value and potential molecular mechanism of AC096751.1 in COAD are still unclear. Methods: Whole genome RNA-sequencing datasets of The Cancer Genome Atlas (TCGA) COAD cohort were collected into current study, comprehensive survival analysis and bioinformatics function enrichment analysis approaches were apply to explore the clinical outcome and molecular mechanisms of AC096751.1 in COAD. Results: In current study, we found that AC096751.1 is markedly down-regulated in COAD cancer tissues (log2 fold change =2.303, P<0.0001, false discovery rate <0.0001), and can be serve as a biomarker to distinguish COAD cancer and paracancerous tissues [area under curve=0.9518, 95% confidence interval (CI)=0.9261-0.9776]. Survival analysis suggests that low expression of AC096751.1 is connected with poor clinical outcome of COAD, and can serve as a novel prognostic indicator (log-rank P=0.016, adjusted P=0.005, hazard ratio=0.548, 95% CI=0.360-0.836). Bioinformatics function enrichment analysis suggests that the molecular mechanism of AC096751.1 in COAD may include participation in cell adhesion, cell proliferation, mitogen-activated protein kinase kinase (MAPKK), MAPK, janus-activated kinase-singal transducers and activators of transcriprion cascade, Erk1 and Erk 2 cascade, and nuclear factor-kappa B pathway. Tumor microenvironment and immune infiltration analysis indicates that COAD patients with different AC096751.1 expression have significant variation in tumor immune background. Conclusion: The present study found that AC096751.1 is significantly differentially expressed in COAD and can be serve as a novel prognostic biomarker.

LncRNA EPR regulates intestinal mucus production and protects against inflammation and tumorigenesis

The long non-coding RNA EPR is expressed in epithelial tissues, binds to chromatin and controls distinct biological activities in mouse mammary gland cells. Because of its high expression in the intestine, in this study we have generated a colon-specific conditional targeted deletion (EPR cKO) to evaluate EPR in vivo functions in mice. EPR cKO mice display epithelium hyperproliferation, impaired mucus production and secretion, as well as inflammatory infiltration in the proximal portion of the large intestine. RNA sequencing analysis reveals a rearrangement of the colon crypt transcriptome with strong reduction of goblet cell-specific factors including those involved in the synthesis, assembly, transport and control of mucus proteins. Further, colon mucosa integrity and permeability are impaired in EPR cKO mice, and this results in higher susceptibility to dextran sodium sulfate (DSS)-induced colitis and tumor formation. Human EPR is down-regulated in human cancer cell lines as well as in human cancers, and overexpression of EPR in a colon cancer cell line results in enhanced expression of pro-apoptotic genes. Mechanistically, we show that EPR directly interacts with select genes involved in mucus metabolism whose expression is reduced in EPR cKO mice and that EPR deletion causes tridimensional chromatin organization changes.

ANKRD29, as a new prognostic and immunological biomarker of non-small cell lung cancer, inhibits cell growth and migration by regulating MAPK signaling pathway

BACKGROUND

The predominant cancer-related deaths worldwide are caused by lung cancer, particularly non-small cell lung cancer (NSCLC), despite the fact that numerous therapeutic initiatives have been devised to improve the outcomes. Ankyrin repeat domain (ANKRD) is one of the widespread protein structural motifs in eukaryotes but the functions of ANKRD proteins in NSCLC progression remains unclear.

METHODS

We performed integrative bioinformatical analysis to determine the dysregulated expression of ANKRDs in multiple tumors and the association between ANKRD29 expression and the NSCLC tumor environment. Quantitative real-time PCR (qRT-PCR), western blot, immunohistochemistry (IHC), and tissue microarray (TMA) assays were used to investigate the expression of ANKRD29 in NSCLC cell lines. The role of ANKRD29 in NSCLC cell proliferation and migration in vitro was deteceted by 5-bromodeoxyuridine (BrdU) incorporation, colony formation, flow cytometry, would-healing, trans-well, and western blot experiment. RNA-seq technology was applied to deciper the molecular mechanism regulated by ANKRD29 in NSCLC.

RESULTS

We constructed a valuable risk-score system for predicting the overall survival outcomes of NSCLC patients based on the expression of five hub ANKRD genes. And we found that the hub gene ANKRD29 was remarkedly decreased in NSCLC tissues and cell lines due to the promoter hypermethylation, and revealed that high ANKRD29 expression obviously correlated with patients' better clinical outcome. Overexpression of ANKRD29 significantly inhibited cell proliferation and migration, promoted the cancerous cells' sensitivity to carboplatin and enhanced the killing ability of T cells in NSCLC cells. Interestingly, ANKRD29 can be served as a biomarker to predict the response to immunotherapy in NSCLC. Mechanically, RNA-seq results showed that ANKRD29 could regulate MAPK signaling pathway. Moreover, we screened two potential agonists for ANKRD29.

CONCLUSIONS

ANKRD29 functions as a new tumor suppressor in NSCLC tumorigenesis and could be developed as a biomarker for prognostic prediction, immunotherapy response, and drug susceptibility evaluation of NSCLC in the future.

Construction and verification of a novel prognostic risk model for kidney renal clear cell carcinoma based on immunity-related genes

Background: Currently, there are no useful biomarkers or prognostic risk markers for the diagnosis of kidney renal clear cell carcinoma (KIRC), although recent research has shown that both, the onset and progression of KIRC, are substantially influenced by immune-associated genes (IAGs). Objective: This work aims to create and verify the prognostic value of an immune risk score signature (IRSS) based on IAGs for KIRC using bioinformatics and public databases. Methods: Differentially expressed genes (DEGs) related to the immune systems (IAGs) in KIRC tissues were identified from The Cancer Genome Atlas (TCGA) databases. The DEGs between the tumor and normal tissues were identified using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a prognostic IRSS model was constructed and its prognostic and predictive performance was analyzed using survival analyses and nomograms. Kidney renal papillary cell carcinoma (KIRP) sets were utilized to further validate this model. Results: Six independent immunity-related genes (PAEP, PI3, SAA2, SAA1, IL20RB, and IFI30) correlated with prognosis were identified and used to construct an IRSS model. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer prognoses than those of patients in the low-risk group in both, the verification set (p <0.049; HR = 1.84; 95% CI = 1.02-3.32) and the training set (p < 0.001; HR = 3.12, 95% CI = 2.23-4.37). The numbers of regulatory T cells (Tregs) were significantly positively correlated with the six immunity-related genes identified, with correlation coefficients were 0.385, 0.415, 0.399, 0.451, 0.485, and 0.333, respectively (p <0.001). Conclusion: This work investigated the association between immune infiltration, immunity-related gene expression, and severity of KIRC to construct and verify a prognostic risk model for KIRC and KIRP.

Ferroptosis: Reviewing CRC with the Third Eye

Colorectal cancer (CRC) has been one of the most common cancers and maintains the second-highest incidence and mortality rates among all cancers. The high risk of recurrence and metastasis and poor survival are still huge challenges in CRC therapy, in which the discovery of ferroptosis provides a novel perspective. It has been ten years since a unique type of regulated cell death driven by iron accumulation and lipid peroxidation was proposed and named ferroptosis. During the past decade, there have been multiple pieces of evidence suggesting that ferroptosis participates in the pathophysiological processes during disease progression. In this review, we describe ferroptosis as an imbalance of oxidant systems and anti-oxidants which results in lipid peroxidation, membrane damage, and finally cell death. We elaborate on the mechanisms of ferroptosis and systematically summarize recent studies on the regulatory pathways of ferroptosis in CRC from various perspectives, ranging from encoding genes, noncoding RNAs to regulatory proteins. Finally, we discuss the potential therapeutic role of ferroptosis in CRC treatments.

LncRNA profiles from Notch signaling: Implications for clinical management and tumor microenvironment of colorectal cancer

The interplay between long non-coding RNAs (lncRNAs) and the Notch pathway involves a variety of malignancies. However, Notch-derived lncRNAs and their latent clinical significance remain elusive in colorectal cancer (CRC). In this study, we introduced a framework that could screen Notch-derived lncRNAs (named “NLncer”) and ultimately identified 24 NLncers. To further explore the clinical significance of these NLncers, we performed LASSO and Cox regression in TCGA-CRC cohort (n = 584) and then retained six lncRNAs tightly associated with prognosis. The final model (termed “NLncS”) was subsequently tested in GSE38832 (n = 122), GSE39582 (n = 573), and an in-house clinical cohort (n = 115). Ultimately, our NLncS model could serve as an independent risk factor and afford a robust performance for assessing the prognosis of CRC patients. Additionally, patients with high NLncS risk scores were characterized by upregulation of immune pathways, strong immunogenicity, abundant CD8 + T-cell infiltration, and potentially higher response rates to CTLA4 blockers, which turned out to be suitable for immunotherapy. Aiming at globally observing the characteristics of high-risk patients, somatic mutation and methylation modification analysis provide us with evidence at the genomic and transcriptomic levels. To facilitate the clinical transformability, we mined deeply into the sensitive compounds targeting high-risk individuals and identified dasatinib as a candidate agent for patients with a high Notch risk score. In conclusion, our NLncS model is a promising biomarker for optimizing the clinical management of CRC patients.

Identification of a Two-lncRNA Signature with Prognostic and Diagnostic Value for Hepatocellular Carcinoma

Background

Accumulating evidence has revealed the important role of long noncoding RNAs (lncRNA) in tumorigenesis and progression of hepatocellular carcinoma (HCC). This study aimed to identify potential lncRNAs that can serve as diagnostic and prognostic signatures for HCC.

Methods

Expression profiling analysis was performed to identify differentially expressed lncRNAs (DElncRNA) between HCC and matched normal samples by integrating two independent microarray datasets. Functional Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were explored by Gene Set Variation Analysis. The prognostic and diagnostic models were developed based on two DElncRNAs. Real-time PCR was used to quantify the relative expressions of candidate lncRNAs.

Results

Two robust DElncRNAs were identified and verified by quantitative PCR between HCC and matched normal samples. Function enrichment analysis revealed that they were associated with the wound healing process. The two lncRNAs were subsequently used to construct a prognostic risk model for HCC. Patients with high-risk scores estimated by the model showed a shorter survival time than low-risk patients (P < 0.001). Besides, the two lncRNA-based HCC diagnostic models exhibited good performance in discriminating HCC from normal samples on both training and test sets. The values of area under the curve (AUC) for early (I–II) and late (III–IV) HCC detection were 0.88 and 0.93, respectively.

Conclusions

The two wound healing-related DElncRNAs showed robust performance for HCC prognostic prediction and detection, implying their potential role as diagnostic and prognostic markers for HCC.

Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma

Pancreatic adenocarcinoma (PAAD) has high mortality and a very poor prognosis. Both surgery and chemotherapy have a suboptimal therapeutic effect, and this caused a need to find new approaches such as immunotherapy. Therefore, it is essential to develop a new model to predict patient prognosis and facilitate early intervention. Our study screened out and validated the target molecules based on the TCGA-PAAD dataset. We established the risk signature using univariate and multivariate Cox regression analysis and used GSE62452 and GSE28735 to verify the accuracy and reliability of the model. Expanded application of PAAD-immune-related genes signature (-IRGS) on other datasets was conducted, and the corresponding nomograms were constructed. We also analyzed the correlation between immune-related cells/genes and potential treatments. Our research demonstrated that a high riskscore of PAAD-IRGS in patients with PAAD was correlated with poor overall survival, disease-specific survival and progression free interval. The same results were observed in patients with LIHC. The models constructed were confirmed to be accurate and reliable. We found various correlations between PAAD-IRGS and immune-related cells/genes, and the potential therapeutic agents. These findings indicate that PAAD-IRGS may be a promising indicator for prognosis and of the tumor-immune microenvironment status in PAAD.

Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma

Background

The survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD).

Methods

The proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data from the cancer genomic maps [The Cancer Genome Atlas (TCGA)] dataset were analyzed to identify co-differentially expressed genes (cDEGs) between recurrence samples and non-recurrence samples in COAD using limma package. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was conducted. Univariate and multivariate Cox regressions were applied to identify the independent prognostic feature cDEGs and establish the signature whose performance was evaluated by Kaplan–Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and calibration curve. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. GSE17538 and GSE39582 were used for external validation. Quantitative real-time PCR and Western blot analysis were carried out to validate our findings.

Results

We identified 86 cDEGs in recurrence samples compared with non-recurrence samples. These genes were primarily enriched in the regulation of carbon metabolic process, fructose and mannose metabolism, and extracellular exosome. Then, an eight-gene-based signature (CA12, HBB, NCF1, KBTBD11, MMAA, DMBT1, AHNAK2, and FBLN2) was developed to separate patients into high- and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Four prognostic clinical features, including pathological M, N, T, and RS model status, were screened for building the nomogram survival model. The PCR and Western blot analysis results suggested that CA12 and AHNAK2 were significantly upregulated, while MMAA and DMBT1 were downregulated in the tumor sample compared with adjacent tissues, and in non-recurrent samples compared with non-recurrent samples in COAD.

Conclusion

These identified recurrence-related gene signatures might provide an effective prognostic predictor and promising therapeutic targets for COAD patients.

LINC01315 accelerates the growth and epithelial-mesenchymal transition of colorectal cancer cells via activating the Wnt/β-catenin signal

The pathological roles of long non-coding RNAs (lncRNAs) in colorectal carcinoma (CRC) have been corroborated. To date, the pathological contributions of LINC01315 in the epithelial-mesenchymal transition (EMT) property of CRC are still ambiguous. By silencing LINC01315, we disclosed that LINC01315 promoted the growth, metastatic characteristics, and the EMT of CRC cells in vitro. Mechanistically, LINC01315 activated Wnt/β-catenin signaling. LINC01315 bound to the β-catenin promoter and activated its transcription. In rescue experiments, ectopic overexpression of β-catenin counteracted the inhibiting effector-triggered by LINC01315 deletion. In summary, this preliminary study brings new insights to the pathological significance of the LINC01315/Wnt/β-catenin signaling pathway in CRC.

A seven-lncRNA signature for predicting prognosis in breast carcinoma

Background

Long non-coding RNAs (lncRNAs) play an important part in tumorigenesis and cancer metastasis and can serve as a potential biosignature for cancer prognosis. However, the use of lncRNA signatures to predict survival in breast carcinoma is yet unreported.

Methods

The lncRNA expression profiles and homologous clinical data of 913 breast carcinoma samples from the Cancer Genome Atlas (TCGA), were analyzed to obtain 2,547 differentially expressed lncRNAs. Univariate Cox proportional risk regression was applied to both the training and testing datasets to screen the common prognostic lncRNAs. Potential prognostic LncRNAs were screened by multivariate Cox proportional risk regression in the training data set of the selected LncRNAs.

Results

Seven lncRNAs (LINC02037, MAPT-AS1, RP1-37C10.3, RP11-344E13.4, RP11-454P21.1, RP11-616M22.1, SPACA6P-AS) were prominently associated with overall survival. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves indicated that these indicators were sensitive and specific for survival prediction. The areas under the ROC curve of the seven-lncRNA signature in predicting 3- and 5-year survival rates were 0.771 and 0.780 respectively in the combined cohort. Furthermore, enrichment analysis revealed that these seven lncRNAs might participate multiple pathways related to tumorigenesis and prognosis.

Conclusions

The proposed seven-lncRNA signature could serve as a latent prognostic biomarker for survival prediction in patients with breast carcinoma.

Comprehensive analysis of an immune infiltrate-related competitive endogenous RNA network reveals potential prognostic biomarkers for non-small cell lung cancer

Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA-miRNA-mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.

Steroid receptor RNA activator gene footprint in the progression and drug resistance of colorectal cancer through oxidative phosphorylation pathway

BACKGROUND

The steroid receptor RNA activator 1 (SRA1) gene is involved in the progression of various cancers via different molecular mechanisms mediated by long non-coding RNA SRA (lncRNA SRA). This study aimed to evaluate the lncRNA SRA effect on the tumor progression of colorectal cancer (CRC).

METHODS

SRA1 expression was assessed in the cancer genome atlas datasets, CRC cell lines, and tumor specimens. Meta-analysis and gene co-expression network analysis were performed to identify pathways related to SRA1. RNA interference and cell treatment were utilized to examine the role of SRA1 expression in HT-29 and Caco-2 cell lines. Also, the effect of SRA1 expression was investigated on drug resistance, clinical parameters, and mutations in CRC samples.

RESULTS

The SRA1 transcripts, especially lncRNA SRA, were dysregulated in CRC tissue samples compared with normal tissue samples. Furthermore, SRA1 depletion decreased colony formation and proliferation while induced apoptosis in HT-29 and Caco-2 cells. In silico analyses indicated that SRA1 level was correlated with expression levels of oxidative phosphorylation (OXPHOS) genes. LncRNA SRA expression increased in response to the increased oxidative capacity, and when lncRNA SRA was knocked down, the expression level of OXPHOS pathway genes, including NDUFB5 and ATP5F1B, was changed. Also, KRAS-mutant samples had the highest SRA1 expression level.

CONCLUSIONS

LncRNA SRA could function as an oncogene through the OXPHOS pathway in CRC, and serve as a potential biomarker for identifying CRC subtype with KRAS mutations. The findings suggest that lncRNA SRA might be a therapeutic target to inhibit cell proliferation in CRC.

Establishment of a novel ferroptosis-related lncRNA pair prognostic model in colon adenocarcinoma

Long non-coding RNAs (lncRNAs) have been reported to be prognostic factors for cancer. Ferroptosis is an iron-dependent process of programmed cell death. Here, we established a ferroptosis-related lncRNA (frlncRNA) pair signature and revealed its prognostic value in colon adenocarcinoma (COAD) by analyzing the data from The Cancer Genome Atlas (TCGA). FrlncRNAs were identified based on co-expression analysis using the Pearson correlation. Differentially expressed frlncRNAs (DEfrlncRNAs) were recognized and paired, followed by prognostic assessment using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) penalized Cox analysis was used to determine and construct a risk score prognostic model, by which the receiver operating characteristic (ROC) curves for predicting the overall survival (OS) were conducted. Following the evaluation of whether it was an independent prognostic factor, correlations between the risk score model and clinicopathological characteristics, hypoxia- and immune-related factors, and somatic variants were investigated. In total, 148 DEfrlncRNA pairs were identified, 25 of which were involved in a risk score prognostic signature. The area under ROC curves (AUCs) representing the predictive effect for 1-, 3-, and 5-year survival rates were 0.860, 0.885, and 0.934, respectively. The risk score model was confirmed as an independent prognostic factor and was significantly superior to the clinicopathological characteristics. Correlation analyses showed disparities in clinicopathological characteristics, hypoxia- and immune-related factors, and somatic variants, as well as specific signaling pathways between high- and low-risk groups. The novel risk score prognostic model constructed by pairing DEfrlncRNAs showed promising clinical prediction value in COAD.

LncRNA MCF2L-AS1 aggravates the malignant development of colorectal cancer via targeting miR-105-5p/RAB22A axis

BACKGROUND

Colorectal cancer (CRC) represents one of the major malignant cancers in the world. It has been demonstrated that long non-coding RNAs (lncRNAs) can cause great influences on various human cancers. Though MCF.2 cell line derived transforming sequence like antisense RNA 1 (MCF2L-AS1) and its carcinogenic effect in CRC has been elucidated by several previous researches, the underlying mechanism remains unknown.

AIM

We aimed at exploring the function and regulatory mechanism of MCF2L-AS1 in CRC.

METHODS

MCF2L-AS1 expression in CRC cells was tested via RT-qPCR assay. The effects of MCF2L-AS1 on the biological properties of CRC cells were testified through functional experiments. The molecular mechanism of MCF2L-AS1 was verified through mechanism experiments.

RESULTS

MCF2L-AS1 was highly expressed in CRC cells, and it could enhance the proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) process of CRC cells. MiR-105-5p was sponged by MCF2L-AS1 in CRC cells and Ras-related protein Rab-22A (RAB22A) was verified to be the downstream target of miR-105-5p. It was verified through rescue assays that RAB22A overexpression or miR-105-5p silencing could reverse the repressive impact of MCF2L-AS1 silencing on CRC progression.

CONCLUSION

MCF2L-AS1 accelerated the malignant development of CRC cells by targeting the miR-105-5p/RAB22A axis.

Esophageal Cancer Associated Immune Genes as Biomarkers for Predicting Outcome in Upper Gastrointestinal Tumors

Esophageal cancer (EC) is the seventh most common tumor in the world, ranking the sixth leading cause of cancer death, with a 5-year survival rate of 15-25%. Therefore, reliable prognostic biomarkers are needed to effectively predict the prognosis of EC. In this study, the gene profile information of the EC cohort served as a training set, which was derived from TCGA and Immport databases. GO and KEGG enrichment analysis was performed on the differential genes in normal and tumor groups of EC. The immune genes in differentially expressed genes (DEGs) were further obtained for univariate and multivariate Cox and Lasso regression analysis, and 6 independent immune genes (S100A3, STC2, HSPA6, CCL25, GPER1, and OSM) associated with prognosis were obtained to establish an immune risk score signature (IRSS). The signature was validated using head and neck cancers (HNSC) and gastric cancer (GC)in upper gastrointestinal malignancies as validation sets. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly favorable than that of the low-risk group in both the training set (P < 0.001; HR = 3.68, 95% CI = 2.14−6.35) and the validation set (P = 0.010; HR = 1.43, 95% CI = 1.09−1.88). A nomogram combining multiple clinical information and IRSS was more effective than a single independent prognostic factor in predicting outcome. This study explored the potential link between immunity and EC, and established and validated prognostic biomarkers that can effectively predict the prognosis of EC, HNSC and GC based on six immune genes.

Construction of a long noncoding RNA-based competing endogenous RNA network and prognostic signatures of left- and right-side colon cancer

Background

Cancers located on the right and left sides of the colon have distinct clinical and molecular characteristics. This study aimed to explore the regulatory mechanisms of location-specific long noncoding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) in colon cancer and identify potential prognostic biomarkers.

Method

Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) between right- and left-side colon cancers were identified by comparing RNA sequencing profiles. Functional enrichment analysis was performed for the DEGs, and a ceRNA network was constructed. Associations between DELs and patient survival were examined, and a DEL-based signature was constructed to examine the prognostic value of these differences. Clinical colon cancer tissues and Gene Expression Omnibus (GEO) datasets were used to validate the results.

Results

We identified 376 DELs, 35 DEMs, and 805 DEGs between right- and left-side colon cancers. The functional enrichment analysis revealed the functions and pathway involvement of DEGs. A ceRNA network was constructed based on 95 DEL–DEM–DEG interactions. Three DELs (LINC01555, AC015712, and FZD10-AS1) were associated with the overall survival of patients with colon cancer, and a prognostic signature was established based on these three DELs. High risk scores for this signature indicated poor survival, suggesting that the signature has prognostic value for colon cancer. Examination of clinical colon cancer tissues and GEO dataset analysis confirmed the results.

Conclusion

The ceRNA regulatory network suggests roles for location-specific lncRNAs in colon cancer and allowed the development of an lncRNA-based prognostic signature, which could be used to assess prognosis and determine treatment strategies in patients with colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01901-3.

Screening of immunosuppressive cells from colorectal adenocarcinoma and identification of prognostic markers

Background: Colorectal cancer (CRC) is the most common type of gastrointestinal malignant tumour. Colorectal adenocarcinoma (COAD) – the most common type of CRC – is particularly dangerous. The role of the immune system in the development of tumour-associated inflammation and cancer has received increasing attention recently.

Methods: In the present study, we compiled the expression profiles of 262 patients with complete follow-up data from The Cancer Genome Atlas (TCGA) database as an experimental group and selected 65 samples from the Gene Expression Omnibus (GEO) dataset (of which 46 samples were with M0) as a verification group. First, we screened the immune T helper 17 (Th17) cells related to the prognosis of COAD. Subsequently, we identified Th17 cells-related hub genes by utilising Weighted Gene Co-expression Network Analysis (WGCNA) and Least Absolute Shrinkage and Selector Operation (LASSO) regression analysis. Six genes associated with the prognosis in patients with COAD were identified, including: KRT23, ULBP2, ASRGL1, SERPINA1, SCIN, and SLC28A2. We constructed a clinical prediction model and analysed its predictive power.

Results: The identified hub genes are involved in developing many diseases and closely linked to digestive disorders. Our results suggested that the hub genes could influence the prognosis of COAD by regulating Th17 cells’ infiltration.

Conclusions: These newly discovered hub genes contribute to clarifying the mechanisms of COAD development and metastasis. Given that they promote COAD development, they may become new therapeutic targets and biomarkers of COAD.

Comprehensive In Silico Analysis of a Novel Serum Exosome-Derived Competitive Endogenous RNA Network for Constructing a Prognostic Model for Glioblastoma

Purpose

Glioblastoma (GBM) is one of the most aggressive brain tumors with high mortality, and tumor-derived exosomes provide new insight into the mechanisms of GBM tumorigenesis, metastasis and therapeutic resistance. We aimed to establish an exosome-derived competitive endogenous RNA (ceRNA) network for constructing a prognostic model for GBM.

Methods

We obtained the expression profiles of long noncoding RNAs (lncRNAs), miRNAs, and mRNAs from the GEO and TCGA databases and identified differentially expressed RNAs in GBM to construct a ceRNA network. By performing lasso and multivariate Cox regression analyses, we identified optimal prognosis-related differentially expressed lncRNAs (DElncRNAs) and generated a risk score model termed the exosomal lncRNA (exo-lncRNA) signature. The exo-lncRNA signature was subsequently validated in the CGGA GBM cohort. Finally, a novel prognostic nomogram was constructed based on the exo-lncRNA signature and clinicopathological parameters and validated in the CGGA external cohort. Based on the ceRNA hypothesis, oncocers were identified based on highly positive correlations between lncRNAs and mRNAs mediated by the same miRNAs. Furthermore, regression analyses were performed to assess correlations between the expression abundances of lncRNAs in tumors and exosomes.

Results

A total of 45 DElncRNAs, six DEmiRNAs, and 38 DEmRNAs were identified, and an exosome-derived ceRNA network was built. Three optimal prognostic-related DElncRNAs, HOTAIR (HR=0.341, P<0.001), SOX21-AS1 (HR=0.30, P<0.001), and STEAP3-AS1 (HR=2.47, P<0.001), were included to construct the exo-lncRNA signature, which was further proven to be an independent prognostic factor. The novel prognostic nomogram was constructed based on the exo-lncRNA signature, patient age, pharmacotherapy, radiotherapy, IDH mutation status, and MGMT promoter status, with a concordance index of 0.878. ROC and calibration plots both suggested that the nomogram had beneficial discrimination and predictive abilities. A total of 11 pairs of prognostic oncocers were identified. Regression analysis suggested excellent consistency of the expression abundance of the three exosomal lncRNAs between exosomes and tumor tissues.

Conclusions

Exosomal lncRNAs may serve as promising prognostic predictors and therapeutic targets. The prognostic nomogram based on the exo-lncRNA signature might provide an intuitive method for individualized survival prediction and facilitate better treatment strategies.

A novel five-lncRNA signature panel improves high-risk survival prediction in patients with cholangiocarcinoma

Cholangiocarcinoma (CCA) is a fatal disease with dismal survival rates. Long non-coding RNA (lncRNA) expression profiling as potential prognostic biomarkers play critical roles in tumor initiation, development, and poor prognosis. Identifying specific lncRNA to predict the prognosis of CCA patients in the early stages is very important for improving a patient’s survival. In the current study, we aimed to establish a novel risk-stratification lncRNA signature panel in CCA. The initial lncRNA discovery was identified in The Cancer Genome Atlas database (TCGA cohort). The Cox regression analysis was used to establish the lncRNA prognostic model and the receiver operating characteristic (ROC) curve analysis was performed to assess the specificity and sensitivity of the model. This was followed by independent validation of the lncRNA signature in the CCA patients from the First Affiliated Hospital of Wenzhou Medical University (WMU cohort). Furthermore, by using the Gene Ontology function and Kyoto Encyclopedia Gene and Genome pathway enrichment analysis, we explored the potential function of prognosis lncRNA. Finally, five lncRNA (HULC; AL359715.5; AC006504.8; AC090114.2; AP00943.4) were screened to establish the predictive model that significantly associated with poor overall survival(HR:4.879;95%CI,1.587-14.996;p=0.006). This five-lncRNA signature model showed excellent accuracy in the TCGA cohort (AUC=0.938), and also robustly predicted survival in the validation WMU cohort(AUC=0.816). Functional enrichment analysis suggested prognostic lncRNA was primarily associated with CCA-related biological processes. Our data established a novel lncRNA signature model for CCA risk-stratification and robust identification of CCA patients with poor molecular genotypes. Moreover, it revealed new molecular mechanisms of CCA.

LINC01315 silencing inhibits the aggressive phenotypes of colorectal carcinoma by sponging miR-205-3p

Long non-coding RNAs (lncRNAs) are important regulatory factors in the progression of cancers. In this study, we investigated the molecular mechanism of long intergenic non-coding 01315 (LINC01315) in inhibiting the aggressive characteristics of colorectal carcinoma (CRC) cells. We proved that LINC01315 was significantly upregulated in CRC. Knockdown of LINC01315 decreased CRC cell growth and invasion in vitro. Bioinformatics analysis and a luciferase reporter experiment showed direct binding between LINC01315 and miR-205-3p. Furthermore, LINC01315 positively modulated protein kinase AMP-activated catalytic subunit α 1 (PRKAA1) expression by serving as a "sponge" for miR-205-3p. Moreover, LINC01315 regulated the growth and invasive phenotypes of CRC cells by sponging miR-205-3p. Downregulation of LINC01315 remarkedly impaired the tumorigenicity of CRC cells in vivo in a transplanted tumour model. Altogether, our results demonstrated that downregulation of LINC01315 suppresses CRC progression by sponging miR-205-3p.

Multiple microarray analyses identify key genes associated with the development of Non-Small Cell Lung Cancer from Chronic Obstructive Pulmonary Disease

Introduction: Chronic obstructive pulmonary disease (COPD) is an independent risk factor of non-small cell lung cancer (NSCLC). This study aimed to analyze the key genes and potential molecular mechanisms that are involved in the development from COPD to NSCLC.

Methods: Expression profiles of COPD and NSCLC in GSE106899, GSE12472, and GSE12428 were downloaded from the Gene Expression Omnibus (GEO) database, followed by identification of the differentially expressed genes (DEGs) between COPD and NSCLC. Based on the identified DEGs, functional pathway enrichment and lung carcinogenesis-related networks analyses were performed and further visualized with Cytoscape software. Then, principal component analysis (PCA), cluster analysis, and support vector machines (SVM) verified the ability of the top modular genes to distinguish COPD from NSCLC. Additionally, the corrections between these key genes and clinical staging of NSCLC were studied using the UALCAN and HPA websites. Finally, a prognostic risk model was constructed based on multivariate Cox regression analysis. Kaplan-Meier survival curves of the top modular genes on the training and verification sets were generated.

Results: A total of 2350, 1914, and 1850 DEGs were obtained from GSE106899, GSE12472, and GSE12428 datasets, respectively. Following analysis of protein-protein interaction networks, the identified modular gene signatures containing H2AFX, MCM2, MCM3, MCM7, POLD1, and RPA1 were identified as markers for discrimination between COPD and NSCLC. The modular gene signatures were mainly enriched in the processes of DNA replication, cell cycle, mismatch repair, and others. Besides, the expression levels of these genes were significantly higher in NSCLC than in COPD, which was further verified by the immunohistochemistry. In addition, the high expression levels of H2AFX, MCM2, MCM7, and POLD1 correlate with poor prognosis of lung adenocarcinoma (LUAD). The Cox regression prognostic risk model showed the similar results and the predictive ability of this model is independent of other clinical variables.

Conclusions: This study revealed several key modules that closely relate to NSCLC with underlying disease COPD, which provide a deeper understanding of the potential mechanisms underlying the malignant development from COPD to NSCLC. This study provides valuable prognostic factors in high-risk lung cancer patients with COPD.

Identification of the 3-lncRNA Signature as a Prognostic Biomarker for Colorectal Cancer

Colorectal cancer (CRC) is one of the most common malignant carcinomas in the world, and metastasis is the main cause of CRC-related death. However, the molecular network involved in CRC metastasis remains poorly understood. Long noncoding RNA (lncRNA) plays a vital role in tumorigenesis and may act as a competing endogenous RNA (ceRNA) to affect the expression of mRNA by suppressing miRNA function. In this study, we identified 628 mRNAs, 144 lncRNAs, and 25 miRNAs that are differentially expressed (DE) in metastatic CRC patients compared with nonmetastatic CRC patients from the Cancer Genome Atlas (TCGA) database. Functional enrichment analyses confirmed that the identified DE mRNAs are extensively involved in CRC tumorigenesis and migration. By bioinformatics analysis, we constructed a metastasis-associated ceRNA network for CRC that includes 28 mRNAs, 12 lncRNAs, and 15 miRNAs. We then performed multivariate Cox regression analysis on the ceRNA-related DE lncRNAs and identified a 3-lncRNA signature (LINC00114, LINC00261, and HOTAIR) with the greatest prognostic value for CRC. Clinical feature analysis and functional enrichment analysis further proved that these three lncRNAs are involved in CRC tumorigenesis. Finally, we used Transwell, Cell Counting Kit (CCK)-8, and colony formation assays to clarify that the inhibition of LINC00114 promotes the migratory, invasive, and proliferative abilities of CRC cells. The results of the luciferase assay suggest that LINC00114 is the direct target of miR-135a, which also verified the ceRNA network. In summary, this study provides a metastasis-associated ceRNA network for CRC and suggests that the 3-lncRNA signature may be a useful candidate for the diagnosis and prognosis of CRC.

A Competing Endogenous RNA Network and an 8-lncRNA Prognostic Signature Identify MYO16-AS1 as an Oncogenic lncRNA in Bladder Cancer

Recently, growing evidence has shed light on the competitive endogenous RNAs (ceRNAs) activity of long noncoding RNAs (lncRNAs) in carcinogenesis and tumor progression. To better elucidate the regulatory mechanisms of lncRNA in muscle-invasive bladder cancer (MIBC), we identified aberrantly expressed mRNAs, lncRNAs, and miRNAs in tumor tissues by using RNA sequence profiles from The Cancer Genome Atlas. The MIBC-specific ceRNA network, including 58 lncRNAs, 22 miRNAs, and 52 mRNAs, was constructed and visualized in Cytoscape. Further, using the univariate and multivariate Cox regression model, we screened 8 lncRNAs (AC078778.1, LINC00525, AC008676.1, AP000553.1, SACS-AS1, AC009065.1, AC127496.3, and MYO16-AS1) to construct an lncRNA signature for predicting the overall survival of MIBC patients. Kaplan-Meier analysis and a receiver operating characteristic curve were applied to evaluate the performance of the signature. Real-time quantitative PCR analysis was carried out to test expression levels of the 8 lncRNAs in MIBC patient tissues. Transwell assays demonstrated that overexpressing MYO16-AS1 can enhance UMUC2 migration and invasion. Our study offers a novel lncRNA-correlated ceRNA model to better understand the molecular mechanisms involved in MIBC. In addition, we developed an independent 8-lncRNAs biomarker for prognostic prediction and identified MYO16-AS1 as an oncogenic lncRNA in bladder cancer.

Long Noncoding RNA LINC01207 Promotes Colon Cancer Cell Proliferation and Invasion by Regulating miR-3125/TRIM22 Axis

Increasing study has validated that long noncoding RNAs (lncRNAs) are involved in the growth and metastasis of colon cancer. LINC01207 has been reported to play vital roles in certain types of cancer, while the precise function of LINC01207 in the progression of colon cancer remains unclear. The objective of this study was to investigate the effect of LINC01207 on the growth and metastasis of colon cancer cells and to explore the underlying mechanism. We found that the expression of LINC01207 was significantly upregulated in colon adenocarcinoma tissues compared with normal tissues by the GEPIA database. Notably, silencing of LINC01207 significantly suppressed the proliferation, migration, and invasion abilities of SW480 and HT-29 cells. Mechanistically, our data demonstrated that LINC01207 could sponge miR-3125 in colon cancer cells. Moreover, miR-3125 could directly target TRIM22 and negatively regulate its expression. Rescue assays revealed that miR-3125 inhibitor or TRIM22 overexpression significantly reversed the repressive role of LINC01207 knockdown in colon cancer cell proliferation and invasion. In conclusion, LINC01207 exerts an oncogenic role in the progression of colon cancer by absorbing miR-3125 to modulating TRIM22 expression.

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

Objective

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

Methods

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

Results

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

Conclusion

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

Screening lncRNAs with diagnostic and prognostic value for human stomach adenocarcinoma based on machine learning and mRNA-lncRNA co-expression network analysis

BACKGROUND

Stomach adenocarcinoma (STAD), is one of the most lethal malignancies around the world. The aim of this study was to find the long noncoding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of STAD.

METHODS

Base on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between STAD and normal tissue. The machine learning and survival analysis were performed to evaluate the potential diagnostic and prognostic value of lncRNAs for STAD. We also build the co-expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real-time polymerase chain reaction (qRT-PCR) and GSE27342 dataset. GSE27342 dataset were also to perform gene set enrichment analysis.

RESULTS

A total of 814 DEmRNAs and 106 DElncRNAs between STAD and normal tissue were obtained. FOXD2-AS1, LINC01235, and RP11-598F7.5 were defined as optimal diagnostic lncRNA biomarkers for STAD. The area under curve (AUC) of the decision tree model, random forests model, and support vector machine (SVM) model were 0.797, 0.981, and 0.983, and the specificity and sensitivity of the three model were 75.0% and 97.1%, 96.9% and 96%, and 96.9% and 97.1%, respectively. Among them, LINC01235 was not only an optimal diagnostic lncRNA biomarkers, but also related to survival time. The expression of three DEmRNAs (ESM1, WNT2, and COL10A1) and three optimal diagnostic lncRNAs biomarkers (FOXD2-AS1, RP11-598F7.5, and LINC01235) in qRT-PCR validation was were consistent with our integrated analysis. Except for FOXD2-AS1, ESM1, WNT2, COL10A1, and LINC01235 were upregulated in STAD, which was consistent with our integration results. Gene set enrichment analysis results indicated that DNA replication, Cell cycle, ECM-receptor interaction, and P53 signaling pathway were four significantly enriched pathways in STAD.

CONCLUSION

Our study identified three DElncRNAs as potential diagnostic biomarkers of STAD. Among them, LINC01235 also was a prognostic lncRNA biomarkers.

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

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

Screening key lncRNAs with diagnostic and prognostic value for head and neck squamous cell carcinoma based on machine learning and mRNA-lncRNA co-expression network analysis

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer around the world. The aim of this study was to seek the long non-coding RNAs (lncRNAs) acting as diagnostic and prognostic biomarker of HNSCC.

METHODS

Base on TCGA dataset, the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified between HNSCC and normal tissue. The machine learning and survival analysis were performed to estimate the potential diagnostic and prognostic value of lncRNAs for HNSCC. We also build the co-expression network and functional annotation. The expression of selected candidate mRNAs and lncRNAs were validated by Quantitative real time polymerase chain reaction (qRT-PCR).

RESULTS

A total of 3363 DEmRNAs (1822 down-regulated and 1541 up-regulated mRNAs) and 32 DElncRNAs (13 down-regulated and 19 up-regulated lncRNAs) between HNSCC and normal tissue were obtained. A total of 13 lncRNAs (IL12A.AS1, RP11.159F24.6, RP11.863P13.3, LINC00941, FOXCUT, RNF144A.AS1, RP11.218E20.3, HCG22, HAGLROS, LINC01615, RP11.351J23.1, AC024592.9 and MIR9.3HG) were defined as optimal diagnostic lncRNAs biomarkers for HNSCC. The area under curve (AUC) of the support vector machine (SVM) model, decision tree model and random forests model and were 0.983, 0.842 and 0.983, and the specificity and sensitivity of the three model were 95.5% and 96.2%, 77.3% and 97.6% and 93.2% and 97.8%, respectively. Among them, AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also related to survival time. The focal adhesion, ECM-receptor interaction, pathways in cancer and cytokine-cytokine receptor interaction were four significantly enriched pathways in DEmRNAs co-expressed with the identified optimal diagnostic lncRNAs. But for most of the selected DEmRNAs and DElncRNAs, the expression was consistent with our integrated analysis results, including LINC00941, LINC01615, FOXCUT, TGA6 and MMP13.

CONCLUSION

AC024592.9, LINC00941, LINC01615 and MIR9-3HG was not only an optimal diagnostic lncRNAs biomarkers, but also were a prognostic lncRNAs biomarkers.

Development of a Nomogram With Alternative Splicing Signatures for Predicting the Prognosis of Glioblastoma: A Study Based on Large-Scale Sequencing Data

Purpose: Alternative splicing (AS) was reported to play a vital role in development and progression of glioblastoma (GBM), the most common and fatal brain tumor. Systematic analysis of survival-associated AS event profiles and prognostic prediction model based on multiple AS events in GBM was needed. Methods: Genome-wide AS and RNA sequencing profiles were generated in 152 patients with GBM in the cancer genome atlas (TCGA). Prognosis-associated AS events were screened by integrated Cox regression analysis to construct the prognostic risk score model in the training cohort (n = 101). The AS-based signature and clinicopathologic parameters were applied to construct a prognostic nomogram for 0.5-, 1-, and 3-year OS prediction. Finally, the regulatory networks between prognostic AS events and splicing factors (SFs) were constructed. Results: A total of 1,598 prognosis-related AS events from 1,183 source genes were determined. Eight prognostic risk score model based on integrated AS events and 7 AS types were established, respectively. Concordance index (C-index) and receiver operating characteristic (ROC) curve analysis demonstrated powerful ability in distinguishing patients' outcomes. Only Alternate Donor site (AD) and Exon Skip (ES) signature out of the eight types of AS signature were identified as independent prognostic factors for GBM, which was validated in the internal validation cohort. The nomogram with age, new event, pharmaceutical therapy, radiation therapy, AD signature and ES signature were constructed, with C-index of 0.892 (95% CI, 0.853-0.931; P = 5.13 × 10^-15). Calibration plots, ROC, and decision curve analysis suggested excellent predictive performance for the nomogram in both TCGA training cohort and validation cohort. Splicing network indicated distinguished correlations between prognostic AS events and SFs in GBM patients. Conclusions: AS-based prediction model could serve as a promising prognostic predictor and potential therapeutic target for GBM, facilitating better treatment strategies in clinical practice.

LINC01207 Predicts Poor Prognosis and Suppresses Cell Growth and Metastasis via Regulating GSK-3β/β-Catenin Signaling Pathway in Malignant Glioma

Background

Recent literature has revealed that LINC01207 plays a vital part in tumorigenesis and malignancy progression. However, the potential mechanisms of LINC01207 in malignant glioma are still unknown.

Material/Methods

Quantitative real-time polymerase chain reaction (qRT-PCR) was applied to analyze LINC01207 mRNA levels in malignant glioma cell lines and tissue samples. The correlation between LINC01207 mRNA levels and clinical characteristics was explored, and the relative survival rate was observed using the Kaplan-Meier method. To examine the function of LINC01207, we performed cell viability, EdU assay, cell cycle assay, Transwell assay, and wound-healing assay to analyze relative cell proliferation, migration/invasion ability. Finally, qRT-PCR and western blot were used to investigate the potential mechanisms.

Results

LINC01207 mRNA was lowly expressed in malignant glioma cells and cancer tissue samples. Low expression of LINC01207 was associated with Karnofsky performance score (KPS), invasion condition, and tumor grade. Moreover, multivariate analysis confirmed LINC01207 expression and tumor grade were significant independent predictors of poor survival in malignant glioma. LINC01207 markedly inhibited cellar proliferation and viability via inducing G0/G1 phase cell cycle arrested and repressed cell metastasis through restraining epithelial-to-mesenchymal procession in vivo. In addition, we detected a reduction in the protein levels of β-catenin and p-GSK-3β, while GSK-3β expression was upregulated.

Conclusions

In summary, LINC01207 served as a tumor-related tumor suppress gene for malignant glioma through inhibiting of GSK-3β/β-catenin signaling pathway.

FOXA1-induced LINC01207 facilitates head and neck squamous cell carcinoma via up-regulation of TNRC6B

Head and neck squamous cell carcinoma (HNSCC) is a common cancer in China, which was mainly caused by smoking and HPV infection. With the advancement of molecular research, it is meaningful to explore the biomarkers of HNSCC. LINC01207 (small integral membrane protein 31, also known as SMIM31) is a verified oncogene in colorectal adenocarcinoma. Present study aimed to explore the function of LINC01207 in HNSCC cells. Function assays including EdU, colony formation, TUNEL and JC-1 assay revealed that LINC01207 was an oncogene in HNSCC cells. Next, by some mechanism assays including RIP assay and luciferase reporter assay, miR-5047 was identified as the downstream gene of LINC01207. Subsequently, trinucleotide repeat containing adaptor 6B (TNRC6B) was verified as the target of miR-5047. LINC01207 boosted HNSCC cell proliferation and stemness characteristics via acting as a ceRNA of TNRC6B to bind miR-5047. Then, we identified that transcription of both LINC01207 and TNRC6B was induced by FOXA1, which played a tumor facilitator role in HNSCC cells. In a word, present study uncovered a novel ceRNA mechanism of LINC01207/miR-5047/TNRC6B in HNSCC cells, which might contribute to HNSCC treatment.

Comprehensive identification of a two-genesignature as a novel potential prognostic model for patients with medulloblastoma

Medulloblastoma is one of the most common malignant pediatric brain tumors and has a poor prognosis and high mortality. We investigated the prognostic significance of specific gene signatures and established a novel prognostic model for medulloblastoma patients. Ninety-seven differentially expressed genes between 69 medulloblastoma samples and 4 normal cerebellum samples were identified using the GSE68956 dataset. Univariate and multivariate Cox regression analyses revealed optimal prognosis-related genes, of which PFKP and STXBP1 exhibited significant prognostic values. A risk score model was then established to assess the prognostic value of the gene signature. Kaplan-Meier survival analysis demonstrated that patients with a high risk score had significantly poorer overall survival (OS, log-rank P = 0.003308). The concordance index (C-index) of the two-gene prognostic model for OS prediction was 0.752 (95% CI, 0.740-0.764). The area under the receiver operating characteristic curve (AUC) values for predicting 3-year and 5-year survival were 0.726 and 0.730, respectively. The risk score model was further validated in the ICGC cohort and PUMCH cohort using quantitative real-time polymerase chain reaction (qRT-PCR). Cox regression analyses were performed to assess the two-gene risk score model, metastasis stage, and chemotherapy as independent prognostic factors for medulloblastoma. The C-index of the comprehensive prognostic model composed of the two-gene signature integrated with clinicopathological features for predicting OS was 0.823 (95% CI, 0.739-0.907). The AUCs of the comprehensive prognostic model for predicting 3-year and 5-year survival were 0.774 and 0.759, respectively. Thus, the two-gene risk score model is a promising prognostic biomarker for medulloblastoma.

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma

BACKGROUND

Recent genome-wide association studies (GWASs) have suggested several susceptibility loci of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) by statistical analysis at individual single-nucleotide polymorphisms (SNPs). However, these loci only explain a small fraction of HBV-related HCC heritability. In the present study, we aimed to identify additional susceptibility loci of HBV-related HCC using advanced knowledge-based analysis.

METHODS

We performed knowledge-based analysis (including gene- and gene-set-based association tests) on variant-level association p-values from two existing GWASs of HBV-related HCC. Five different types of gene-sets were collected for the association analysis. A number of SNPs within the gene prioritized by the knowledge-based association tests were selected to replicate genetic associations in an independent sample of 965 cases and 923 controls.

RESULTS

The gene-based association analysis detected four genes significantly or suggestively associated with HBV-related HCC risk: SLC39A8, GOLGA8M, SMIM31, and WHAMMP2. The gene-set-based association analysis prioritized two promising gene sets for HCC, cell cycle G1/S transition and NOTCH1 intracellular domain regulates transcription. Within the gene sets, three promising candidate genes (CDC45, NCOR1 and KAT2A) were further prioritized for HCC. Among genes of liver-specific expression, multiple genes previously implicated in HCC were also highlighted. However, probably due to small sample size, none of the genes prioritized by the knowledge-based association analyses were successfully replicated by variant-level association test in the independent sample.

CONCLUSIONS

This comprehensive knowledge-based association mining study suggested several promising genes and gene-sets associated with HBV-related HCC risks, which would facilitate follow-up functional studies on the pathogenic mechanism of HCC.

Identification of an Immune-Related Nine-lncRNA Signature Predictive of Overall Survival in Colon Cancer

Growing evidence suggests that immune-related genes (IRGs) and long non-coding RNAs (lncRNAs) can serve as prognostic markers of overall survival (OS) in patients with colon cancer. This study aimed to identify an immune-related lncRNA signature for the prospective assessment of prognosis in these patients. Gene expression and clinical data of colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Immune-related lncRNAs were identified by a correlation analysis between IRGs and lncRNAs. In total, 447 samples were divided into a training cohort (224 samples) and a testing cohort (223 samples). Univariate, lasso and multivariate Cox regression analyses identified an immune-related nine-lncRNA signature closely related to OS in colon cancer patients in the training dataset. A risk score formula involving nine immune-related lncRNAs was developed to evaluate the prognostic value of the lncRNA signature in the training dataset. Colon cancer patients with a high risk score had poorer OS than those with a low risk score. A multivariate Cox regression analysis confirmed that the immune-related nine-lncRNA signature could be an independent prognostic factor in colon cancer patients. The results were further confirmed in the testing cohort and the entire TCGA cohort. Furthermore, a gene set enrichment analysis revealed several pathways with significant enrichment in the high- and low-risk groups that may be helpful in formulating clinical strategies and understanding the underlying mechanisms. Finally, a quantitative real-time polymerase chain reaction assay found that the nine lncRNAs were significantly differentially expressed in colon cancer cell lines. The results of this study indicate that this signature has important clinical implications for improving predictive outcomes and guiding individualized treatment in colon cancer patients. These lncRNAs could be potential biomarkers affecting the prognosis of colon cancer.

Prognostic value of small nucleolar RNAs (snoRNAs) for colon adenocarcinoma based on RNA sequencing data

Although the molecular studies of single gastrointestinal tumors have been widely reported by media, it is not clear about the function of small nucleolar RNA (snoRNA) in the progression, development and prognostic significance in colon adenocarcinoma, and its certain molecular mechanisms and functions remain to be studied. This study aims to dig out the gene expression data profile of colon adenocarcinoma and construct the prognostic molecular pathology prediction-evaluation, ultimately revealing the clinical prognostic value of snoRNA in colon adenocarcinoma. 932 differentially expressed snoRNAs of the colon adenocarcinoma were obtained by edgeR R package. Only 4 prognostically-significant snoRNAs (SNORD14E, SNORD67, SNORD12C, and SNORD17) (P < 0.05) were discovered after univariate COX regression mode analysis. Moreover, through multivariate COX regression mode analysis, 2 prognostically-significant snoRNAs (SNORD14E and SNORD67) (P < 0.05) were obtained. Using the above 473 COAD samples, a prognostic model of risk score was constructed. The inflection point of the prognostic risk score acted as a boundary to divide the patients into high-risk and low-risk groups. The K-M survival curve of the prognostic model of risk score revealed that high risk group has a lower survival rate (P < 0.05). The research has successfully provided valuable prognostic factors and prognostic models for patients with malignant colon tumor.

Bioinformatic profiling identifies a platinum-resistant-related risk signature for ovarian cancer

Most high‐grade serous ovarian cancer (HGSOC) patients develop resistance to platinum‐based chemotherapy and recur. Many biomarkers related to the survival and prognosis of drug‐resistant patients have been delved by mining databases; however, the prediction effect of single‐gene biomarker is not specific and sensitive enough. The present study aimed to develop a novel prognostic gene signature of platinum‐based resistance for patients with HGSOC. The gene expression profiles were obtained from Gene Expression Omnibus and The Cancer Genome Atlas database. A total of 269 differentially expressed genes (DEGs) associated with platinum resistance were identified (P < .05, fold change >1.5). Functional analysis revealed that these DEGs were mainly involved in apoptosis process, PI3K‐Akt pathway. Furthermore, we established a set of seven‐gene signature that was significantly associated with overall survival (OS) in the test series. Compared with the low‐risk score group, patients with a high‐risk score suffered poorer OS (P < .001). The area under the curve (AUC) was found to be 0.710, which means the risk score had a certain accuracy on predicting OS in HGSOC (AUC > 0.7). Surprisingly, the risk score was identified as an independent prognostic indicator for HGSOC (P < .001). Subgroup analyses suggested that the risk score had a greater prognostic value for patients with grade 3‐4, stage III‐IV, venous invasion and objective response. In conclusion, we developed a seven‐gene signature relating to platinum resistance, which can predict survival for HGSOC and provide novel insights into understanding of platinum resistance mechanisms and identification of HGSOC patients with poor prognosis.

Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma

Glioblastoma (GBM) is the most common brain tumor with significant morbidity and mortality. Autophagy plays a vital role in GBM development and progression. We aimed to establish an autophagy-related multigene expression signature for individualized prognosis prediction in patients with GBM. Differentially expressed autophagy-related genes (DE-ATGs) in GBM and normal samples were screened using TCGA. Univariate and multivariate Cox regression analyses were performed on DE-ATGs to identify the optimal prognosis-related genes. Consequently, NRG1 (HR=1.142, P=0.008), ITGA3 (HR=1.149, P=0.043), and MAP1LC3A (HR=1.308, P=0.014) were selected to establish the prognostic risk score model and validated in the CGGA validation cohort. GSEA revealed that these genes were mainly enriched in cancer- and autophagy-related KEGG pathways. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P= 6.955×10^-5). The autophagy signature was identified as an independent prognostic factor. Finally, a prognostic nomogram including the autophagy signature, age, pharmacotherapy, radiotherapy, and IDH mutation status was constructed, and TCGA/CGGA-based calibration plots indicated its excellent predictive performance. The autophagy-related three-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for GBM. The prognostic nomogram could assist individualized survival prediction and improve treatment strategies.

Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples

BACKGROUND

Long non-coding RNAs (lncRNAs) are emerging as important regulators of various biological processes. While many studies have exploited public resources such as RNA sequencing (RNA-Seq) data in The Cancer Genome Atlas to study lncRNAs in cancer, it is crucial to choose the optimal method for accurate expression quantification.

RESULTS

In this study, we compared the performance of pseudoalignment methods Kallisto and Salmon, alignment-based transcript quantification method RSEM, and alignment-based gene quantification methods HTSeq and featureCounts, in combination with read aligners STAR, Subread, and HISAT2, in lncRNA quantification, by applying them to both un-stranded and stranded RNA-Seq datasets. Full transcriptome annotation, including protein-coding and non-coding RNAs, greatly improves the specificity of lncRNA expression quantification. Pseudoalignment methods and RSEM outperform HTSeq and featureCounts for lncRNA quantification at both sample- and gene-level comparison, regardless of RNA-Seq protocol type, choice of aligners, and transcriptome annotation. Pseudoalignment methods and RSEM detect more lncRNAs and correlate highly with simulated ground truth. On the contrary, HTSeq and featureCounts often underestimate lncRNA expression. Antisense lncRNAs are poorly quantified by alignment-based gene quantification methods, which can be improved using stranded protocols and pseudoalignment methods.

CONCLUSIONS

Considering the consistency with ground truth and computational resources, pseudoalignment methods Kallisto or Salmon in combination with full transcriptome annotation is our recommended strategy for RNA-Seq analysis for lncRNAs.

LncRNA-AWPPH activates TGF-β1 in colorectal adenocarcinoma

Long non-coding RNA associated with poor prognosis of hepatocellular carcinoma (lncRNA-AWPPH) is a newly discovered lncRNA that has important functions in the pathogenesis of several malignancies. However, its role in the development of colorectal adenocarcinoma is unknown. The current study therefore investigated the function of AWPPH in colorectal adenocarcinoma. A total of 86 patients with colorectal adenocarcinoma and 56 healthy controls were included. Tumor tissues and adjacent healthy tissues were collected from patients with colorectal adenocarcinoma, and blood was collected from both patients and healthy controls. Expression of AWPPH in tissues and blood was detected by the reverse transcription-quantitative polymerase chain reaction. Receiver operating characteristic curve analysis was used to evaluate the diagnostic value of serum AWPPH for colorectal adenocarcinoma. All patients were followed up for 5 years, and survival curve analysis was performed to investigate the association between serum level of AWPPH and patients' survival. The effects of AWPPH overexpression and silencing in colorectal adenocarcinoma cell lines were investigated. Effects on cell proliferation and viability were detected by the cell counting kit-8 and MTT assays, respectively. Effects on transforming growth factor β1 (TGF-β1) expression were determined by western blotting. AWPPH was significantly upregulated in tumor tissues compared with adjacent healthy tissues. AWPPH expression levels in blood increased in patients with colorectal adenocarcinoma compared with healthy controls, suggesting that AWPPH may be a sensitive and accurate diagnostic and prognostic biomarker for colorectal adenocarcinoma. AWPPH overexpression in colorectal adenocarcinoma cell lines promoted cell proliferation and increased cell viability, while AWPPH silencing resulted in opposite effects. AWPPH overexpression promoted and silencing inhibited TGF-β1 expression. Therefore, lncRNA-AWPPH promoted colorectal adenocarcinoma by promoting tumor growth, increasing tumor cell viability and activating the TGF-β1 signaling.

Single-cell cloning of human T-cell lines reveals clonal variation in cell death responses to chemotherapeutics

Genetic modification of human leukemic cell lines using CRISPR-Cas9 has become a staple of gene-function studies. Single-cell cloning of modified cells is frequently used to facilitate studies of gene function. Inherent in this approach is an assumption that the genetic drift, amplified in some cell lines by mutations in DNA replication and repair machinery, as well as non-genetic factors will not introduce significant levels of experimental cellular heterogeneity in clones derived from parental populations. In this study, we characterize the variation in cell death of fifty clonal cell lines generated from human Jurkat and MOLT-4 T-cells edited by CRISPR-Cas9. We demonstrate a wide distribution of sensitivity to chemotherapeutics between non-edited clonal human leukemia T-cell lines, and also following CRISPR-Cas9 editing at the NLRP1 locus, or following transfection with non-targeting sgRNA controls. The cell death sensitivity profile of clonal cell lines was consistent across experiments and failed to revert to the non-clonal parental phenotype. Whole genome sequencing of two clonal cell lines edited by CRISPR-Cas9 revealed unique and shared genetic variants, which had minimal read support in the non-clonal parental population and were not suspected CRISPR-Cas9 off-target effects. These variants included genes related to cell death and drug metabolism. The variation in cell death phenotype of clonal populations of human T-cell lines may be a consequence of T-cell line genetic instability, and to a lesser extent clonal heterogeneity in the parental population or CRISPR-Cas9 off-target effects not predicted by current models. This work highlights the importance of genetic variation between clonal T-cell lines in the design, conduct, and analysis of experiments to investigate gene function after single-cell cloning.

Prognosis of clear cell renal cell carcinoma (ccRCC) based on a six-lncRNA-based risk score: an investigation based on RNA-sequencing data

Background

The scientific understanding of long non-coding RNAs (lncRNAs) has improved in recent decades. Nevertheless, there has been little research into the role that lncRNAs play in clear cell renal cell carcinoma (ccRCC). More lncRNAs are assumed to influence the progression of ccRCC via their own molecular mechanisms.

Methods

This study investigated the prognostic significance of differentially expressed lncRNAs by mining high-throughput lncRNA-sequencing data from The Cancer Genome Atlas (TCGA) containing 13,198 lncRNAs from 539 patients. Differentially expressed lncRNAs were assessed using the R packages edgeR and DESeq. The prognostic significance of lncRNAs was measured using univariate Cox proportional hazards regression. ccRCC patients were then categorized into high- and low-score cohorts based on the cumulative distribution curve inflection point the of risk score, which was generated by the multivariate Cox regression model. Samples from the TCGA dataset were divided into training and validation subsets to verify the prognostic risk model. Bioinformatics methods, gene set enrichment analysis, and protein–protein interaction networks, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses were subsequently used.

Results

It was found that the risk score based on 6 novel lncRNAs (CTA-384D8.35, CTD-2263F21.1, LINC01510, RP11-352G9.1, RP11-395B7.2, RP11-426C22.4) exhibited superior prognostic value for ccRCC. Moreover, we categorized the cases into two groups (high-risk and low-risk), and also examined related pathways and genetic differences between them. Kaplan–Meier curves indicated that the median survival time of patients in the high-risk group was 73.5 months, much shorter than that of the low-risk group (112.6 months; P < 0.05). Furthermore, the risk score predicted the 5-year survival of all 539 ccRCC patients (AUC at 5 years, 0.683; concordance index [C-index], 0.853; 95% CI 0.817–0.889). The training set and validation set also showed similar performance (AUC at 5 years, 0.649 and 0.681, respectively; C-index, 0.822 and 0.891; 95% CI 0.774–0.870 and 0.844–0.938).

Conclusions

The results of this study can be applied to analyzing various prognostic factors, leading to new possibilities for clinical diagnosis and prognosis of ccRCC.

Long non-coding RNA LINC01207 silencing suppresses AGR2 expression to facilitate autophagy and apoptosis of pancreatic cancer cells by sponging miR-143-5p

Pancreatic cancer is a serious malignancy accompanied by a well-documented poor prognosis. Accumulating studies have indicated the crucial roles played by long non-coding RNAs (lncRNAs) in proliferation, apoptosis and invasion of cancer cells. The aim of the current study was to investigate the role of lncRNA LINC01207 in autophagy and apoptosis of pancreatic cancer cells and its regulatory mechanism interacting with miR-143-5p. Initially, expression profiles of lncRNAs and genes associated with pancreatic cancer were identified. The expression patterns of LINC01207, miR-143-5p and AGR2 in both pancreatic cancer and adjacent tissues were then determined. The binding relationship of LINC01207 to miR-143-5p and targeting relationship of miR-143-5p to AGR2 were subsequently verified. Silencing of LINC01207, or up-regulation or down-regulation of miR-143-5p was introduced into the pancreatic cancer cells, so as to analyze their effects on the cell growth, apoptosis and autophagy. Besides, these regulatory effects were further explored with the determination of the autophagy- and apoptosis-related gene or proteins. LINC01207 and AGR2 were highly expressed while miR-143-5p was poorly expressed in pancreatic cancer. Functionally, LINC01207 can bind to miR-143-5p, and AGR2 was a target gene of miR-143-5p. Importantly, silencing of LINC01207 down-regulated the expression of AGR2 by up-regulating miR-143-5p. Moreover, silencing of LINC01207 and up-regulation of miR-143-5p promoted cell apoptosis and autophagy, corresponding to increased expression of autophagy- and apoptosis-related proteins, in addition to inhibited cell growth. Taken together, silencing of LINC01207 prevents the progression of pancreatic cancer by impairing miR-143-5p-targeted AGR2 expression, providing a potential target for pancreatic cancer treatment.

Competitive endogenous RNA network identifies four long non-coding RNA signature as a candidate prognostic biomarker for lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most commonly histological subtype of lung cancer (LC) and the prognoses of the majority of LUAD patients are still very poor. The present study aimed at integrating long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA) expression data to construct lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network and identify importantly potential lncRNA signature in ceRNA network as a candidate prognostic biomarker for LUAD patients.

METHODS

lncRNA, miRNA and mRNA expression data as well as clinical characteristics of LUAD patients were retrieved from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs (DElncRNAs), differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNA (DEmiRNA) between LUAD and normal lung tissues samples were analyzed. A lncRNA-miRNA-mRNA ceRNA network was constructed and the biological functions of DEmRNAs in ceRNA network were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Univariate and multivariate Cox regression analyses of DElncRNAs in ceRNA network were implemented to predict the overall survival (OS) in LUAD patients. The receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model.

RESULTS

A total of 1,664 DElncRNAs, 120 DEmiRNAs and 2,503 DEmRNAs was identified between LUAD and normal lung tissues samples. A lncRNA-miRNA-mRNA ceRNA network including 140 DElncRNAs, 33 DEmiRNAs and 57 DEmRNAs was established. Kaplan-Meier (KM) [Log-rank (LR) test] and univariate regression analysis of those 140 DElncRNAs revealed that 7 DElncRNAs (LINC00518, UCA1, NAV2-AS2, MED4-AS1, SYNPR-AS1, AC011483.1, AP002478.1) were simultaneously identified to be associated with OS of LUAD patients. A multivariate Cox regression analysis of those 7 DElncRNAs showed that a group of 4 DElncRNAs including AP002478.1 (Cox P=4.66E-03), LINC00518 (Cox P=2.34E-04), MED4-AS1 (Cox P=6.42E-03) and NAV2-AS2 (Cox P=6.66E-02) had significantly prognostic value in OS of LUAD patients. The cumulative risk score indicated that the 4-lncRNA signature was significantly associated with OS of LUAD patients (P=0). The area under the curve (AUC) of the 4-lncRNA signature related with 3-year survival was 0.669.

CONCLUSIONS

The present study provides novel insights into the lncRNA-related regulatory mechanisms in LUAD, and identifying 4-lncRNA signature may serve as a candidate prognostic biomarker in predicting the OS of LUAD patients.

Overexpression and mutation of ZNF384 is associated with favorable prognosis in breast cancer patients

Background

To search for genes with high sensitivity and to explore its application value related to clinical prognostic prediction, so as to provide important foundation for the preventive intervention, early diagnosis, treatment and prognosis evaluation for breast cancer.

Methods

Tissue samples from ten clinical breast cancer patients were collected to search for the common mutant genes among various samples, and to explore the enrichment degree of mutant genes at both disease and signaling pathway levels using the whole exome sequencing (WES). Subsequently, targets genes with changes in expression levels that showed high correlations with mutation were screened from the above common genes using The Cancer Genome Atlas (TCGA) database. On this basis, differences in the mutation and expression levels of the screened target genes between breast cancer tissues and para-carcinoma tissues, as well as their correlations with patient survival were analyzed using the gene expression and mutation data in TCGA database, together with the clinical information. Finally, the potential regulatory pathways and potential downstream targets of the target genes were predicted through gene set enrichment analysis (GSEA) using Multi-Experiment Matrix (MEM) software.

Results

A total of 23 common mutant genes were discovered from the tissue samples from ten breast cancer patients, which were mostly enriched in the cancer, PI3K/Akt and cAMP signaling pathways. Among these 23 genes, only the changes in the expression levels of ZNF384 and PDE4DIP had displayed over 15% consistency with mutation. Besides, it was discovered through TCGA database analysis that, the expression level of ZNF384 gene in breast cancer tissues with ZNF384 mutation was far higher than that in those with no ZNF384 mutation. Moreover, such gene mutation and high expression had shown significantly positive correlation with the patient survival (P<0.05). In addition, GSEA indicated that, tissues with high ZNF384 expression were associated with enrichments related to cell cycle signaling pathway and mitosis metaphase pathway, while this series of effects might be correlated with its regulation on the level and activity of its downstream gene CXCL14.

Conclusions

ZNF384 mutation and up-regulated ZNF384 expression level in breast cancer tissues is significantly positively correlated with patient survival. Therefore, ZNF384 can serve as a molecular marker for the diagnosis and prognostic prediction of breast cancer as well as a potential therapeutic target.

Six-mRNA risk score system and nomogram constructed for patients with ovarian cancer

Platinum is a commonly used drug for the treatment of ovarian cancer (OC). The aim of the current study was to design and construct a risk score system for predicting the prognosis of patients with OC receiving platinum chemotherapy. The mRNA sequencing data and copy number variation (CNV) information (training set) of patients with OC were downloaded from The Cancer Genome Atlas database. A validation set, GSE63885, was obtained from Gene Expression Omnibus database. The differentially expressed genes (DEGs) and CNV genes (DECNs) between platinum-resistant and platinum-sensitive groups were identified using the limma package. The intersection between DEGs and DECNs were selected. Cox regression analysis was used to identify the genes and clinical factors associated with prognosis. Risk score system assessment and nomogram analysis were performed using the survival and rms packages in R. Gene Set Enrichment Analysis was used to identify the enriched pathways in high and low risk score groups. From 1,144 DEGs and 1,864 DECNs, 48 genes that occurred in the two datasets were selected. A total of six independent prognostic genes (T-box transcription factor T, synemin, tektin 5, growth differentiation factor 3, solute carrier family 22 member 3 and calcium voltage-gated channel subunit α1 C) and platinum response status were revealed to be associated with prognosis. Based on the six independent prognostic genes, a risk score system was constructed and assessed. Nomogram analysis revealed that the patients with the sensitive status and low risk scores had an improved prognosis. Furthermore, the current study revealed that the 574 DEGs identified were involved in eight pathways, including chemokine signaling pathway, toll-like receptor signaling pathway, cytokine-cytokine receptor interaction, RIG I like receptor signaling pathway, natural killer cell mediated cytotoxicity, apoptosis, T cell receptor signaling pathway and Fc ε receptor 1 signaling pathway. The six-mRNA risk score system designed in the present study may be used as prognosis predictor in patients with OC, whereas the nomogram may be valuable for identifying patients with OC who may benefit from platinum chemotherapy.