Characterization of long non-coding RNAs to reveal potential prognostic biomarkers in hepatocellular carcinoma

Inhibitor of DNA Binding Protein 3 (ID3) and Nuclear Respiratory Factor 1 (NRF1) Mediated Transcriptional Gene Signatures are Associated with the Severity of Cerebral Amyloid Angiopathy

Cerebral amyloid angiopathy (CAA) is a degenerative vasculopathy. We have previously shown that transcription regulating proteins- inhibitor of DNA binding protein 3 (ID3) and the nuclear respiratory factor 1 (NRF1) contribute to vascular dysregulation. In this study, we have identified sex specific ID3 and NRF1-mediated gene networks in CAA patients diagnosed with Alzheimer's Disease (AD). High expression of ID3 mRNA coupled with low NRF1 mRNA levels was observed in the temporal cortex of men and women CAA patients. Low NRF1 mRNA expression in the temporal cortex was found in men with severe CAA. High ID3 expression was found in women with the genetic risk factor APOE4. Low NRF1 expression was also associated with APOE4 in women with CAA. Genome wide transcriptional activity of both ID3 and NRF1 paralleled their mRNA expression levels. Sex specific differences in transcriptional gene signatures of both ID3 and NRF1 were observed. These findings were further corroborated by Bayesian machine learning and the GeNIe simulation models. Dynamic machine learning using a Monte Carlo Markov Chain (MCMC) gene ordering approach revealed that ID3 was associated with disease severity in women. NRF1 was associated with CAA and severity of this disease in men. These findings suggest that aberrant ID3 and NRF1 activity presumably plays a major role in the pathogenesis and severity of CAA. Further analyses of ID3- and NRF1-regulated molecular drivers of CAA may provide new targets for personalized medicine and/or prevention strategies against CAA.

Single-cell RNA sequencing revealed subclonal heterogeneity and gene signatures of gemcitabine sensitivity in pancreatic cancer

Introduction: Resistance to gemcitabine is common and critically limits its therapeutic efficacy in pancreatic ductal adenocarcinoma (PDAC). Methods: We constructed 17 patient-derived xenograft (PDX) models from PDAC patient samples and identified the most notable responder to gemcitabine by screening the PDX sets in vivo. To analyze tumor evolution and microenvironmental changes pre- and post-chemotherapy, single-cell RNA sequencing (scRNA-seq) was performed. Results: ScRNA-seq revealed that gemcitabine promoted the expansion of subclones associated with drug resistance and recruited macrophages related to tumor progression and metastasis. We further investigated the particular drug-resistant subclone and established a gemcitabine sensitivity gene panel (GSGP) (SLC46A1, PCSK1N, KRT7, CAV2, and LDHA), dividing PDAC patients into two groups to predict the overall survival (OS) in The Cancer Genome Atlas (TCGA) training dataset. The signature was successfully validated in three independent datasets. We also found that 5-GSGP predicted the sensitivity to gemcitabine in PDAC patients in the TCGA training dataset who were treated with gemcitabine. Discussion and conclusion: Our study provides new insight into the natural selection of tumor cell subclones and remodeling of tumor microenvironment (TME) cells induced by gemcitabine. We revealed a specific drug resistance subclone, and based on the characteristics of this subclone, we constructed a GSGP that can robustly predict gemcitabine sensitivity and prognosis in pancreatic cancer, which provides a theoretical basis for individualized clinical treatment.

Long non-coding RNAs as critical regulators and novel targets in cervical cancer: current status and future perspectives

Cervical cancer is among the leading causes of cancer-associated mortality in women. In spite of vaccine availability, improved screening procedures, and chemoradiation therapy, cervical cancer remains the most commonly diagnosed cancer in 23 countries and the leading cause of cancer deaths in 36 countries. There is, therefore, a need to come up with novel diagnostic and therapeutic targets. Long non-coding RNAs (lncRNAs) play a remarkable role in genome regulation and contribute significantly to several developmental and disease pathways. The deregulation of lncRNAs is often observed in cancer patients, where they are shown to affect multiple cellular processes, including cell cycle, apoptosis, angiogenesis, and invasion. Many lncRNAs are found to be involved in the pathogenesis as well as progression of cervical cancer and have shown potency to track metastatic events. This review provides an overview of lncRNA mediated regulation of cervical carcinogenesis and highlights their potential as diagnostic and prognostic biomarkers as well as therapeutic targets for cervical cancer. In addition, it also discusses the challenges associated with the clinical implication of lncRNAs in cervical cancer.

Identification of Hypothalamic Long Noncoding RNAs Associated with Hypertension and the Behavior/Neurological Phenotype of Hypertensive ISIAH Rats

Long noncoding RNAs (lncRNAs) play an important role in the control of many physiological and pathophysiological processes, including the development of hypertension and other cardiovascular diseases. Nonetheless, the understanding of the regulatory function of many lncRNAs is still incomplete. This work is a continuation of our earlier study on the sequencing of hypothalamic transcriptomes of hypertensive ISIAH rats and control normotensive WAG rats. It aims to identify lncRNAs that may be involved in the formation of the hypertensive state and the associated behavioral features of ISIAH rats. Interstrain differences in the expression of seven lncRNAs were validated by quantitative PCR. Differential hypothalamic expression of lncRNAs LOC100910237 and RGD1562890 between hypertensive and normotensive rats was shown for the first time. Expression of four lncRNAs (Snhg4, LOC100910237, RGD1562890, and Tnxa-ps1) correlated with transcription levels of many hypothalamic genes differentially expressed between ISIAH and WAG rats (DEGs), including genes associated with the behavior/neurological phenotype and hypertension. After functional annotation of these DEGs, it was concluded that lncRNAs Snhg4, LOC100910237, RGD1562890, and Tnxa-ps1 may be involved in the hypothalamic processes related to immune-system functioning and in the response to various exogenous and endogenous factors, including hormonal stimuli. Based on the functional enrichment analysis of the networks, an association of lncRNAs LOC100910237 and Tnxa-ps1 with retinol metabolism and an association of lncRNAs RGD1562890 and Tnxa-ps1 with type 1 diabetes mellitus are proposed for the first time. Based on a discussion, it is hypothesized that previously functionally uncharacterized lncRNA LOC100910237 is implicated in the regulation of hypothalamic processes associated with dopaminergic synaptic signaling, which may contribute to the formation of the behavioral/neurological phenotype and hypertensive state of ISIAH rats.

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.

A Ductal-Cell-Related Risk Model Integrating Single-Cell and Bulk Sequencing Data Predicts the Prognosis of Patients With Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy. Single-cell sequencing (scRNA-seq) technology enables quantitative gene expression measurements that underlie the phenotypic diversity of cells within a tumor. By integrating PDAC scRNA-seq and bulk sequencing data, we aim to extract relevant biological insights into the ductal cell features that lead to different prognoses. Firstly, differentially expressed genes (DEGs) of ductal cells between normal and tumor tissues were identified through scRNA-seq data analysis. The effect of DEGs on PDAC survival was then assessed in the bulk sequencing data. Based on these DEGs (LY6D, EPS8, DDIT4, TNFSF10, RBP4, NPY1R, MYADM, SLC12A2, SPCS3, NBPF15) affecting PDAC survival, a risk score model was developed to classify patients into high-risk and low-risk groups. The results showed that the overall survival was significantly longer in the low-risk group (p < 0.05). The model also revealed reliable predictive power in different subgroups of patients. The high-risk group had a higher tumor mutational burden (TMB) (p < 0.05), with significantly higher mutation frequencies in KRAS and ADAMTS12 (p < 0.05). Meanwhile, the high-risk group had a higher tumor stemness score (p < 0.05). However, there was no significant difference in the immune cell infiltration scores between the two groups. Lastly, drug candidates targeting risk model genes were identified, and seven compounds might act against PDAC through different mechanisms. In conclusion, we have developed a validated survival assessment model, which acted as an independent risk factor for PDAC.

Long noncoding RNA VPS9D1-AS1 promotes esophageal squamous cell carcinoma progression via the Wnt/β-catenin signaling pathway

The VPS9D1 antisense RNA1 (VPS9D1-AS1, lncRNA MYU) can act as an oncogene or an antioncogene in different malignancies. In the present study, we demonstrated that VPS9D1-AS1 is significantly upregulated in esophageal squamous cell carcinoma (ESCC) and assessed its biological function and clinical prognosis. RNA-sequencing was conducted in four pairs of ESCC tissues and normal adjacent tissues (NATs). Compared with controls, lncRNA VPS9D1-AS1 was highly expressed in ESCC tissues, cell lines and plasma. VPS9D1-AS1 upregulation significantly correlated with the histopathological grade and clinical stage of ESCC. Analyses revealed poor prognosis in ESCC patients with high VPS9D1-AS1 expression. VPS9D1-AS1 knockdown led to the inhibition of tumor proliferation, migration, and invasion in vivo and vitro. VPS9D1-AS1 silencing downregulated the Wnt/β-catenin signaling pathways by acting on key proteins such as β-catenin and c-Myc. However, the expressions of these proteins increased after the addition of pathway agonist CT99021. Therefore, taken together VPS9D1-AS1 is highly expressed in ESCC and its expression can lead to poor prognosis. In conclusion, this study suggested that VPS9D1-AS1 acts as a vital part in facilitating ESCC progression and can be a potential biomarker for the diagnosis of patients with ESCC.

Comprehensive Analysis of Competing Endogenous RNA Network Focusing on Long Noncoding RNA Involved in Cirrhotic Hepatocellular Carcinoma

The role of long noncoding RNAs- (lncRNAs-) associated competing endogenous RNA (ceRNA) in the field of hepatocellular carcinoma (HCC) biology is well established, but the involvement of lncRNAs competing interactions in the progression of liver cirrhosis to HCC is still unclear. We aimed to explore the differential expression profiles of lncRNAs, microRNAs (miRNA), and messenger RNAs (mRNAs) to construct a functional ceRNA network in cirrhotic HCC. The lncRNA, miRNA, and mRNA expression datasets were obtained from Gene Expression Omnibus and The Cancer Genome Atlas. Based on miRanda and TargetScan, the HCC-specific ceRNA network was constructed to illustrate the coexpression regulatory relationship of lncRNAs, miRNAs, and mRNAs. The potential prognostic indicators in the network were confirmed by survival analysis and validated by qRT-PCR. A total of 74 lncRNAs, 36 intersection miRNAs, and 949 mRNAs were differentially expressed in cirrhotic HCC samples compared with cirrhosis samples. We constructed a ceRNA network, including 47 lncRNAs, 35 miRNAs, and 168 mRNAs. Survival analysis demonstrated that 2 lncRNAs (EGOT and SERHL), 4 miRNAs, and 40 mRNAs were significantly associated with the overall survival of HCC patients. Two novel regulatory pathways, EGOT-miR-32-5p-XYLT2 axis and SERHL-miR-1269a/miR-193b-3p-BCL2L1/SYK/ARNT/CHST3/LPCAT1 axis, were built up and contribute to the underlying mechanism of HCC pathogenesis. The higher-expressed SERHL was associated with a higher risk of all-cause death. The expressions of SERHL-miR-1269a-BCL2L1 were significantly different using qRT-PCR in vitro studies. lncRNAs EGOT and SERHL might serve as effective prognostic biomarkers and potential therapeutic targets in cirrhotic HCC treatment.

The Impact of Long Non-Coding RNAs in the Pathogenesis of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is among the utmost deadly human malignancies. This type of cancer has been associated with several environmental, viral, and lifestyle risk factors. Among the epigenetic factors which contribute in the pathogenesis of HCC is dysregulation of long non-coding RNAs (lncRNAs). These transcripts modulate expression of several tumor suppressor genes and oncogenes and alter the activity of cancer-related signaling axes. Several lncRNAs such as NEAT1, MALAT1, ANRIL, and SNHG1 have been up-regulated in HCC samples. On the other hand, a number of so-called tumor suppressor lncRNAs namely CASS2 and MEG3 are down-regulated in HCC. The interaction between lncRNAs and miRNAs regulate expression of a number of mRNA coding genes which are involved in the pathogenesis of HCC. H19/miR-15b/CDC42, H19/miR-326/TWIST1, NEAT1/miR-485/STAT3, MALAT1/miR-124-3p/Slug, MALAT1/miR-195/EGFR, MALAT1/miR-22/SNAI1, and ANRIL/miR-144/PBX3 axes are among functional axes in the pathobiology of HCC. Some genetic polymorphisms within non-coding regions of the genome have been associated with risk of HCC in certain populations. In the current paper, we describe the recent finding about the impact of lncRNAs in HCC.

Gene Expression Changes in the Ventral Tegmental Area of Male Mice with Alternative Social Behavior Experience in Chronic Agonistic Interactions

Daily agonistic interactions of mice are an effective experimental approach to elucidate the molecular mechanisms underlying the excitation of the brain neurons and the formation of alternative social behavior patterns. An RNA-Seq analysis was used to compare the ventral tegmental area (VTA) transcriptome profiles for three groups of male C57BL/6J mice: winners, a group of chronically winning mice, losers, a group of chronically defeated mice, and controls. The data obtained show that both winners and defeated mice experience stress, which however, has a more drastic effect on defeated animals causing more significant changes in the levels of gene transcription. Four genes (Nrgn, Ercc2, Otx2, and Six3) changed their VTA expression profiles in opposite directions in winners and defeated mice. It was first shown that Nrgn (neurogranin) expression was highly correlated with the expression of the genes involved in dopamine synthesis and transport (Th, Ddc, Slc6a3, and Drd2) in the VTA of defeated mice but not in winners. The obtained network of 31 coregulated genes, encoding proteins associated with nervous system development (including 24 genes associated with the generation of neurons), may be potentially useful for studying their role in the VTA dopaminergic neurons maturation under the influence of social stress.

Description of CRISPR/Cas9 development and its prospect in hepatocellular carcinoma treatment

Hepatocellular carcinoma (HCC) is one of the most common malignancies today. Patients suffer from HCC since its high malignancy and limited treatment means. With the development of genetic research, new therapeutic strategy comes up in the way of gene editing. Clustered regularly interspaced short palindromic repeat/CRISPR-associated nuclease 9 (CRISPR/Cas9) was discovered as an immune sequence in bacteria and archaea. After artificial transformation and follow-up research, it is widely used as a gene editing tool. In this review, the development of CRISPR/Cas9 is summarized in retrospect. Through the evaluation of novel research in HCC, it is concluded that CRISPR/Cas9 would promote cancer research and provide a new tool for genetic treatment in prospect.

Risk Scoring System based on lncRNA Expression for Predicting Survival in Hepatocellular Carcinoma with Cirrhosis

Objective:

This study aims to explore the roles of long non-coding RNAs (lncRNAs) for predicting survival in hepatocellular carcinoma (HCC) patients with cirrhosis.

Methods:

A set of lncRNAs differentially expressed between HCC patients with or without cirrhosis was identified using expression profiles of The Cancer Genome Atlas database, and these lncRNAs were screened for their risk scoring system to predict recurrence-free survival (RFS) or overall survival (OS). Predictive ability of risk scoring systems was confirmed using uni- and multivariate Cox analyses while adjusting for clinical features. Predictive lncRNAs were analyzed by functional enrichment analysis.

Results:

Our screen identified 22 lncRNAs that were upregulated in the presence of cirrhosis and 59 that were downregulated. To predict OS of HCC patients with cirrhosis, a risk scoring system was developed with four lncRNAs (LINC02086, LINC00880, LINC01549 and AC136475.3); to predict RFS in these patients, the risk scoring system contained five lncRNAs (SH3RF3-AS1, AC104117.3, AC136475.3, LINC00239 and MRPL23-AS1). All risk scoring systems were associated with an area under the receiver operating characteristic curve > 0.7. Based on uni- and multivariate Cox analyses, the risk scoring system could serve as a significant independent predictor for OS in HCC patients with cirrhosis. Functional enrichment analysis suggested that the lncRNAs in the risk scoring systems are involved primarily in the pathway of Wnt signal and cytokine-cytokine receptor interaction.

Conclusion:

Risk scoring systems based on lncRNAs can effectively predict OS of HCC patients with cirrhosis. The system should be further developed and validated in larger, preferably multi-site patient populations.

Identification of a Novel Eight-lncRNA Prognostic Signature for HBV-HCC and Analysis of Their Functions Based on Coexpression and ceRNA Networks

Studies have demonstrated the prognosis potential of long noncoding RNAs (lncRNAs) for hepatocellular carcinoma (HCC), but specific lncRNAs for hepatitis B virus- (HBV-) related HCC have rarely been reported. This study was aimed at identifying a lncRNA prognostic signature for HBV-HCC and exploring their underlying functions. The sequencing dataset was collected from The Cancer Genome Atlas database as the training set, while the microarray dataset was obtained from the European Bioinformatics Institute database (E-TABM-36) as the validation set. Univariate and multivariate Cox regression analyses identified that eight lncRNAs (TSPEAR-AS1, LINC00511, LINC01136, MKLN1-AS, LINC00506, KRTAP5-AS1, ZNF252P-AS1, and THUMPD3-AS1) were significantly associated with overall survival (OS). These eight lncRNAs were used to construct a risk score model. The Kaplan-Meier survival curve results showed that this risk score can significantly differentiate the OS between the high-risk group and the low-risk group. Receiver operating characteristic curve analysis demonstrated that this risk score exhibited good prediction effectiveness (area under the curve (AUC) = 0.990 for the training set; AUC = 0.903 for the validation set). Furthermore, this lncRNA risk score was identified as an independent prognostic factor in the multivariate analysis after adjusting other clinical characteristics. The crucial coexpression (LINC00511-CABYR, THUMPD3-AS1-TRIP13, LINC01136-SFN, LINC00506-ANLN, and KRTAP5-AS1/TSPEAR-AS1/MKLN1-AS/ZNF252P-AS1-MC1R) or competing endogenous RNA (THUMPD3-AS1-hsa-miR-450a-TRIP13) interaction axes were identified to reveal the possible functions of lncRNAs. These genes were enriched into cell cycle-related biological processes or pathways. In conclusion, our study identified a novel eight-lncRNA prognosis signature for HBV-HCC patients and these lncRNAs may be potential therapeutic targets.

Immune signature of T follicular helper cells predicts clinical prognostic and therapeutic impact in lung squamous cell carcinoma

Lung cancer is the leading reason of cancer-related death from cancer globally for both men and women. Recently, tumor immune heterogeneity has been implicated in cancer clinical outcome. However, this prognostic significance of immune cell types in lung squamous cell carcinoma (LUSC) is unclear and should be systematically investigated. Two microarray datasets (GSE67061 and GSE2088) from the Gene Expression Omnibus (GEO) database were downloaded and then integrated to estimate the fraction of 22 immune cell types by CIBERSORT algorithm. To validate the estimation for LUSC, the data of LUSC TCGA were also assessed in order to determine specific infiltrating immune cell type closely correlated with LUSC patients' survival determined by Cox regression analyses. Immunotherapeutic and chemotherapeutic response between the LUSC patients were also evaluated. T follicular helper cells were obtained by Cox regression analysis to develop the prognostic signature. According to this immune prognostic risk score, immune signature of T follicular helper cells is an independent and specific prognostic signature for predictions of LUSC patient overall survival. Moreover, high-risk group exhibited less expression of N6-methyladenosine (m⁶A) RNA methylation regulator including ALKBH5, METTL3, HNRNPC and KIAA1429 and was much more sensitive to immunotherapy and chemotherapy. This study suggests that this immune signature is important determinants of prognosis in LUSC and may provide potential prognostic biomarker or therapeutic target for immunotherapeutic and chemotherapeutic development.

Risk scoring based on expression of long non‑coding RNAs can effectively predict survival in hepatocellular carcinoma patients with or without fibrosis

Patients with hepatocellular carcinoma (HCC) have different prognoses depending on whether or not they also have fibrosis. Since long non-coding RNAs (lncRNAs) affect tumor formation and progression, the present study aimed to investigate whether their expression might help predict the survival of patients with HCC. Expression profiles downloaded from The Cancer Genome Atlas database were examined to identify lncRNAs differentially expressed (DElncRNAs) between HCC patients with or without fibrosis. These DElncRNAs were then used to develop a risk scoring system to predict overall survival (OS) or recurrence-free survival (RFS). A total of 142 significant DElncRNAs were identified using data from 135 patients with fibrosis and 72 without fibrosis. For HCC patients with fibrosis, a risk scoring system to predict OS was constructed based on five lncRNAs (AL359853.1, Z93930.3, HOXA-AS3, AL772337.1 and AC012640.3), while the risk scoring system to predict RFS was based on 12 lncRNAs (PLCE1-AS1, Z93930.3, LINC02273, TRBV11-2, HHIP-AS1, AC004687.1, LINC01857, AC004585.1, AP000808.1, CU638689.4, AC090152.1 and AL357060.1). For HCC patients without fibrosis, the risk scoring system to predict OS was established based on seven lncRNAs (LINC00239, AC104971.4, AP006285.2, HOXA-AS3, AC079834.2, NRIR and LINC01929), and the system to predict RFS was based on five lncRNAs (AC021744.1, NRIR, LINC00487, AC005858.1 and AC107398.3). Areas under the receiver operating characteristic curves for all risk scoring systems exceeded 0.7. Uni- and multivariate Cox analyses showed that the risk scoring systems were significant independent predictors of OS for HCC patients with fibrosis, or of OS and RFS for HCC patients without fibrosis, after adjusting for clinical factors. Functional enrichment analysis suggested that, depending on the risk scoring system, highly associated genes were involved in pathways mainly associated with the cell cycle, chemokines, Th17 cell differentiation or thermogenesis. The findings of the present study indicate that risk scoring systems based on lncRNA expression can effectively predict the OS of HCC patients with fibrosis as well as the OS or RFS of HCC patients without fibrosis.

A TP53-associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

The tumor-suppressor gene tumor protein p53 (TP53) is one of the most commonly mutated genes in human lung cancer, and TP53 mutations are associated with a worsened prognosis and causes resistance to cancer therapy. RNA sequencing and TP53 mutation data were downloaded to determine specific TP53-associated signature based on differentially expressed genes between patients with lung squamous cell carcinoma (LUSC) with wild type (TP53 ^WT) and mutated (TP53^MUT) TP53. We investigated the predictive value of this signature on the immune microenvironment, tumor mutational burden (TMB), and likelihood of response to immunotherapy and chemotherapy. In total, 1,556 differentially expressed genes were identified based on TP53 mutation status. Three genes (KLK6, MUC22 and CSN1S1) identified by univariate and multivariate Cox regression analyses, comprised the prognostic signature which was an independent and specific prognostic marker of overall survival in patients with LUSC. A nomogram was also established to validate this signature for clinical use. Moreover, the high-risk group was characterized by increased infiltration of monocytes and macrophages M1, and decreased T cells CD8 and T cells follicular helper. High-risk group exhibited a higher TMB, and was much more sensitive to immunotherapy and chemotherapy. KLK6 and CSN1S1 expression and the prognostic prediction values were further validated in clinical samples. The derived TP53-associated signature is a specific and independent prognostic biomarker for LUSC patients, and could provide potential prognostic biomarker or therapeutic targets for the development of novel immunotherapies and chemotherapies.

A novel seven‑miRNA prognostic model to predict overall survival in head and neck squamous cell carcinoma patients

Head and neck squamous cell carcinoma (HNSCC) is highly prevalent worldwide, and the outcome of HNSCC is still difficult to predict due to the lack of appropriate prognostic markers. In the present study, a prognostic model based on a miRNA panel was established to better predict the survival of HNSCC patients. miRNA expression data and clinical information regarding HNSCC patients were acquired from The Cancer Genome Atlas (TCGA) database. Accompanying clinical data was obtained from the University of California, Santa Cruz (UCSC) Xena browser. Using this data, 140 differentially expressed miRNAs (DEMs) were identified between HNSCC tissue samples (n=525) and adjacent normal tissue samples (n=44). The present prognostic model included seven miRNAs (i.e. hsa‑miR‑499a, hsa‑miR‑548k, hsa‑miR‑3619, hsa‑miR‑99a, hsa‑miR‑137, hsa‑miR‑3170, and hsa‑miR‑654), which were identified using least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. The independence of the predictive power of this model was validated by further analyses using clinical information. The outstanding performance of the seven‑miRNA prognostic model was confirmed by time‑dependent receiver operating characteristic curve (ROC) analysis. These results indicated that combining the miRNA panel with pathological characteristics may provide a more accurate prognosis for HNSCC. Functional identification of the target genes of the focal miRNAs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed. The present study demonstrated that the novel miRNA panel reported here may be useful in making different prognoses and may improve the clinical management of patients with HNSCC.

Identification and potential mechanisms of a 4-lncRNA signature that predicts prognosis in patients with laryngeal cancer

Purpose

This study aimed to describe the use of a novel 4-lncRNA signature to predict prognosis in patients with laryngeal cancer and to explore its possible mechanisms.

Methods

We identified lncRNAs that were differentially expressed between 111 tumor tissue samples and 12 matched normal tissue samples from The Cancer Genome Atlas Database (TCGA). We used Cox regression analysis to identify lncRNAs that were correlated with prognosis. A 4-lncRNA signature was developed to predict the prognosis of patients with laryngeal cancer. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to verify the validity of this Cox regression model, and an independent prognosis analysis was used to confirm that the 4-lncRNA signature was an independent prognostic factor. Furthermore, the function of these lncRNAs was inferred using related gene prediction and Gene ontology (GO) enrichment analysis in order to clarify the possible mechanisms underlying their predictive ability.

Results

In total, 214 differentially expressed lncRNAs were identified, and a 4-lncRNA signature was constructed using Cox survival analysis. The risk coefficients in the multivariate Cox analysis revealed that LINC02154 and MNX1-AS1 are risk factors for laryngeal cancer, whereas MYHAS and LINC01281 appear to be protective factors. The results of a functional annotation analysis suggested that the mechanisms by which these lncRNAs influence prognosis in laryngeal cancer may involve the extracellular exosome, the Notch signaling pathway, voltage-gated calcium channels, and the Wnt signaling pathway.

Conclusion

We identified a novel 4-lncRNA signature that can predict the prognosis of patients with laryngeal cancer and that may influence the prognosis of laryngeal cancer by regulating immunity, tumor apoptosis, metastasis, invasion, and other characteristics through the Notch signaling pathway, voltage-gated calcium channels, and the Wnt signaling pathway.

Electronic supplementary material

The online version of this article (10.1186/s40246-019-0230-6) contains supplementary material, which is available to authorized users.

Comprehensive bioinformatics analysis reveals potential lncRNA biomarkers for overall survival in patients with hepatocellular carcinoma: an on-line individual risk calculator based on TCGA cohort

Background

Accumulated evidences have demonstrated that long non-coding RNAs (lncRNAs) are correlated with prognosis of patients with hepatocellular carcinoma. The current study aimed to develop and validate a prognostic lncRNA signature to improve the prediction of overall survival in hepatocellular carcinoma patients.

Methods

The study cohort involved 348 hepatocellular carcinoma patients with lncRNA expression information and overall survival information. Through gene mining approach, the current study established a prognostic lncRNA signature (named LncRNA risk prediction score) for predicting the overall survival of hepatocellular carcinoma patients.

Results

The current study built a predictive nomogram based on ten prognostic lncRNA predictors through Cox regression analysis. In model group, the Harrell’s concordance indexes of LncRNA risk prediction score were 0.811 (95% CI 0.769–0.853) for 1-year overall survival, 0.814 (95% CI 0.772–0.856) for 3-year overall survival and 0.796 (95% CI 0.754–0.838) for 5-year overall survival respectively. In validation cohort, the Harrell’s concordance indexes of LncRNA risk prediction score were 0.779 (95% CI 0.737–0.821), 0.828 (95% CI 0.786–0.870) and 0.796 (95%CI 0.754–0.838) for 1-year survival, 3-year survival and 5-year survival respectively. LncRNA risk prediction score could stratify hepatocellular carcinoma patients into low risk group and high risk group. Further survival curve analysis demonstrated that the overall survival rate of high risk patients was significantly poorer than that of low risk patients (P < 0.001).

Conclusions

In conclusion, the current study developed and validated a prognostic signature to predict the individual mortality risk for hepatocellular carcinoma patients. LncRNA risk prediction score is helpful to identify the patients with high mortality risk and optimize the individualized treatment decision. The web calculator can be used by click the following URL: https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_HCC_3/.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0890-2) contains supplementary material, which is available to authorized users.

Identification of a 26-lncRNAs Risk Model for Predicting Overall Survival of Cervical Squamous Cell Carcinoma Based on Integrated Bioinformatics Analysis

As a common malignancy in women, cervical squamous cell carcinoma is a major cause of cancer-related mortality globally. Recent studies have demonstrated that long non-coding RNA (lncRNA) can function as potential biomarkers in cancer prognosis; however, little is known about its role in cervical cancer. In this study, we downloaded the gene expression profiles along with the clinical data of patients with cervical squamous cell carcinoma from The Cancer Genome Atlas. By applying bioinformatics analysis including random forest selection and Least Absolute Shrinkage and Selection Operator (LASSO) cox regression model along with 10-fold cross-validation, we constructed a 26-lncRNAs risk model that can be used to predict the overall survival of cervical squamous cell carcinoma. After that, Kaplan-Meier analysis combined with log-rank p test was applied to assess the predictive accuracy of the 26-lncRNAs risk model. Further analysis showed that the prognostic value of 26-lncRNAs risk model was independent of other clinicopathological factors. At last, lncRNAs in the model were put into gene ontology biological process enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways analysis, which suggested that these lncRNAs might contribute to cancer-associated processes such as cell cycle and apoptosis. This study indicated that lncRNAs signature could be a useful marker to predict the prognosis of cervical squamous cell carcinoma.

Genomewide identification of a novel six-LncRNA signature to improve prognosis prediction in resectable hepatocellular carcinoma

The current prognostic long noncoding RNA (lncRNA) signatures for hepatocellular carcinoma (HCC) are still controversial and need to be optimized by systematic bioinformatics analyses with suitable methods and appropriate patients. Therefore, we performed the study to establish a credible lncRNA signature for HCC outcome prediction and explore the related mechanisms. Based on the lncRNA profile and the clinical data of carefully selected HCC patients (n = 164) in TCGA, six of 12727 lncRNAs, MIR22HG, CTC‐297N7.9, CTD‐2139B15.2, RP11‐589N15.2, RP11‐343N15.5, and RP11‐479G22.8 were identified as the independent predictors of patients’ overall survival in HCC by sequential univariate Cox and 1000 times Cox LASSO regression with 10‐fold CV, and multivariate Cox analysis with 1000 times bootstrapping. In the Kaplan‐Meier analysis with patients trichotomized by the six‐lncRNA signature, high‐risk patients showed significantly shorter survival than mid‐ and low‐risk patients (log‐rank test P < 0.0001). According to the ROCs, the six‐lncRNA signature showed superior predictive capacity than the two existing four‐lncRNA combinations and the traditional prognostic clinicopathological parameter TNM stage. Furthermore, low MIR22HG and CTC‐297N7.9, but high CTD‐2139B15.2, RP11‐589N15.2, RP11‐343N15.5, and RP11‐479G22.8, were, respectively, demonstrated to be related with the malignant phenotypes of HCC. Functionally, the six lncRNAs were disclosed to involve in the regulation of multiple cell cycle and stress response‐related pathways via mediating transcription regulation and chromatin modification. In conclusion, our study identified a novel six‐lncRNA signature for resectable HCC prognosis prediction and indicated the underlying mechanisms of HCC progression and the potential functions of the six lncRNAs awaiting further elucidation.

Long noncoding RNA VPS9D1-AS1 overexpression predicts a poor prognosis in non-small cell lung cancer

Long noncoding RNAs (LncRNAs) have been reported to play vital roles in non-small cell lung cancer (NSCLC). Recently, LncRNA/VPS9D1-AS1 has been reported to be overexpressed in various cancers. In this study, we aimed to investigate its expression pattern and clinical significance and further evaluate its prognostic value for NSCLC. VPS9D1-AS1 expression was examined in 184 NSCLC patients using a highly sensitive in situ hybridization protocol (RNAscope), and the expression values were correlated with the clinicopathological features. Another cohort including 12 NSCLC patients was used to validate the differential expression of VPS9D1-AS1 by qRT-PCR. TCGA datasets were further used to validate the main findings. We found that the levels of VPS9D1-AS1 were significantly higher in cancer tissues than in paired normal tissues from both lung squamous cell carcinoma (SCC) and adenocarcinoma (ADC) (P < 0.001). Importantly, the levels of VPS9D1-AS1 in patients with lung SCC were significantly higher than those in patients with lung ADC. The high levels of VPS9D1-AS1 were found to be associated with cancer lymph node metastasis (P = 0.020). Prognostic analysis revealed that the survival time for SCC patients with high levels of VPS9D1-AS1 was significantly shorter than that of patients with low levels of VPS9D1-AS1 (P = 0.007). Therefore, our findings suggest that the overexpression of VPS9D1-AS1 serves as a promising biomarker to predict the prognosis of NSCLC.