Five key lncRNAs considered as prognostic targets for predicting pancreatic ductal adenocarcinoma

Drivers of cancer metastasis - Arise early and remain present

Cancer and its metastases arise from mutations of genes, drivers that promote a tumor's growth. Analyses of driver events provide insights into cancer cell history and may lead to a better understanding of oncogenesis. We reviewed 27 metastatic research studies, including pan-cancer studies, individual cancer studies, and phylogenetic analyses, and summarized our current knowledge of metastatic drivers. All of the analyzed studies had a high level of consistency of driver mutations between primary tumors and metastasis, indicating that most drivers appear early in cancer progression and are maintained in metastatic cells. Additionally, we reviewed data from around 50,000 metastatic cancer patients and compiled a list of genes altered in metastatic lesions. We performed Gene Ontology analysis and confirmed that the most significantly enriched processes in metastatic lesions were the epigenetic regulation of gene expression, signal transduction, cell cycle, programmed cell death, DNA damage, hypoxia and EMT. In this review, we explore the most recent discoveries regarding genetic factors in the advancement of cancer, specifically those that drive metastasis.

Long non-coding RNA MIR600HG as a ceRNA inhibits the pancreatic cancer progression through regulating the miR-1197/PITPNM3 axis

Objective

Pancreatic cancer (PC) is considered to be a highly malignant cancer with poor prognosis. Long non-coding RNAs (lncRNAs) is the potential factor to predict cancer prognosis. The effect of MIR600HG in PC needs to be further studied. Our work mainly focused on the importance of MIR600HG for PC prognosis and its underlying molecular mechanism of regulating PC progression.

Methods

Data set was acquired from TCGA database to find differentially expressed genes and prognostic significance of MIR600HG in PC, and to construct the MIR600HG competitive endogenous RNA (ceRNA). Clinical specimens were collected to prove the analysis results. Vector over-expressed MIR600HG was transfected to study the roles of MIR600HG in proliferation, apoptosis, invasion and migration. The methods of CCK-8, flow cytometry, Transwell and scratch assays were all used in order to explore the apoptosis, migration and invasion. We evaluated the proliferation-related genes (PCNA, CyclinD1 and P27), as well as invasion and migration-related genes such as MMP-9, MMP-7 and ICAM-1. The transcriptional regulation between MIR600HG and miR-1197/PITPNM3 axis was determined with luciferase reporter assays.

Results

In present study, MIR600HG was dropped in both PC tissues and cells, and the down-regulated MIR600HG was closely related to the poor clinical outcomes in PC patients. MIR600HG could inhibit proliferation, migration and invasion in PC cells. We also investigated whether MIR600HG acting as a sponge of microRNA-1197 (miR-1197) and miR-1197 acting on PITPNM3. We found the positive association between MIR600HG and PITPNM3, as well as the negative association of miR-1197 and MIR600HG (or PITPNM3). Moreover, PITPNM3 mRNA and protein expression saw a simultaneous increase after the MIR600HG-overexpression (MIR600HG-OE), but this result partially diminished in MIR600HG-OE cells and miR-1197 mimics.

Conclusions

Our study explored the anticancer action of MIR600HG in PC by regulating miR-1197 to increase the expression of PITPNM3, which might help the diagnosis and therapy of PC.

Identification of MIR600HG/hsa-miR-342-3p/ANLN network as a potential prognosis biomarker associated with lmmune infiltrates in pancreatic cancer

Pancreatic cancer is one of the tumors with the worst prognosis, causing serious harm to human health. The RNA network and immune response play an important role in tumor progression. While a systematic RNA network linked to the tumor immune response remains to be further explored in pancreatic cancer. Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, the MIR600HG/hsa-miR-342-3p/ANLN network was determined. WB and IHC were used to confirm the high expression of ANLN in pancreatic cancer. The prognostic model based on the RNA network could effectively predict the survival prognosis of patients. The analysis of immune infiltration showed that the MIR600HG/hsa-miR-342-3p/ANLN network altered the level of infiltration of T helper 2 (Th2) and effector memory T (Tem) cells. Furthermore, we found that the chemokines chemokine ligand (CCL) 5 and CCL14 may play a key role in immune cell infiltration mediated by the RNA network. In conclusion, this study constructed a prognostic model based on the MIR600HG/hsa-miR-342-3p/ANLN network and found that it may function in tumor immunity.

Sex-Specific Associations of MDM2 and MDM4 Variants with Risk of Multiple Primary Melanomas and Melanoma Survival in Non-Hispanic Whites

MDM2-SNP309 (rs2279744), a common genetic modifier of cancer incidence in Li-Fraumeni syndrome, modifies risk, age of onset, or prognosis in a variety of cancers. Melanoma incidence and outcomes vary by sex, and although SNP309 exerts an effect on the estrogen receptor, no consensus exists on its effect on melanoma. MDM2 and MDM4 restrain p53-mediated tumor suppression, independently or together. We investigated SNP309, an a priori MDM4-rs4245739, and two coinherited variants, in a population-based cohort of 3663 primary incident melanomas. Per-allele and per-haplotype (MDM2_SNP309-SNP285; MDM4_rs4245739-rs1563828) odds ratios (OR) for multiple-melanoma were estimated with logistic regression models. Hazard ratios (HR) for melanoma death were estimated with Cox proportional hazards models. In analyses adjusted for covariates, females carrying MDM4-rs4245739*C were more likely to develop multiple melanomas (ORper-allele = 1.25, 95% CI 1.03-1.51, and Ptrend = 0.03), while MDM2-rs2279744*G was inversely associated with melanoma-death (HRper-allele = 0.63, 95% CI 0.42-0.95, and Ptrend = 0.03). We identified 16 coinherited expression quantitative loci that control the expression of MDM2, MDM4, and other genes in the skin, brain, and lungs. Our results suggest that MDM4/MDM2 variants are associated with the development of subsequent primaries and with the death of melanoma in a sex-dependent manner. Further investigations of the complex MDM2/MDM4 motif, and its contribution to the tumor microenvironment and observed associations, are warranted.

Identification of immune and Toll-like receptor signaling pathway related feature lncRNAs to construct diagnostic nomograms for acute ischemic stroke

We aimed to identify the immune and Toll-like receptor (TLR) signaling pathway related feature lncRNAs to construct the diagnostic nomograms for acute ischemic stroke (AIS). Two AIS-associated expression profiles GSE16561 and GSE22255 were downloaded from NCBI Gene Expression Omnibus, the former was the training set and the latter was the validation set. The differential expression genes (DEGs) and lncRNAs (DElncRNAs) related to TLR signaling pathway were identified between AIS and control groups. The single sample gene set enrichment analysis (ssGSEA) was applied to evaluate the immune infiltration. The immune and TLR signaling pathway related DElncRNAs were determined. Three optimization algorithms were utilized to select the immune and TLR signaling pathway related feature lncRNAs to construct the diagnostic nomograms of AIS. Based on the lncRNA signature, a ceRNA network was constructed. 37 DEGs and 28 DElncRNAs related to TLR signaling pathway were identified in GSE16561. 16 immune cell types exhibited significant differences in distribution between AIS and control groups. 28 immune and TLR signaling pathway related DElncRNAs were determined. 8 immune and TLR signaling pathway related feature lncRNAs were selected. The diagnostic nomograms of AIS performed well in both datasets. A ceRNA network was constructed consisting of 7 immune and TLR signaling pathway related feature lncRNAs as well as 19 AIS related miRNAs and 21 TLR signaling pathway related genes. LINC00173, LINC01089, LINC02210, MIR600HG, SNHG14, TP73-AS1, LINC00680 and CASC2 may be the potential biomarkers of AIS diagnosis, and TLR signaling pathway may be a promising immune related therapeutic target for AIS.

An immune-related prognostic model predicts neoplasm-immunity interactions for metastatic nasopharyngeal carcinoma

Background

The prognosis of nasopharyngeal carcinoma (NPC) has been recognized to improve immensely owing to radiotherapy combined with chemotherapy. However, patients with metastatic NPC have a poor prognosis. Immunotherapy has dramatically prolonged the survival of patients with NPC. Hence, further research on immune-related biomarkers is imperative to establish the prognosis of metastatic NPC.

Methods

10 NPC RNA expression profiles were generated from patients with or without distant metastasis after chemoradiotherapy from the Fujian Cancer Hospital. The differential immune-related genes were identified and validated by immunohistochemistry analysis. The method of least absolute shrinkage and selection operator (LASSO)was used to further establish the immune-related prognostic model in an external GEO database (GSE102349, n=88). The immune microenvironment and signal pathways were evaluated in multiple dimensions at the transcriptome and single-cell levels.

Results

1328 differential genes were identified, out of which 520 were upregulated and 808 were downregulated. Notably, most of the immune genes and pathways were down-regulated in the metastasis group. A prognostic immune model involving nine hub genes. Patients in low-risk group were characterized by survival advantage, hot immune phenotype and benefit from immunotherapy. Compared with immune cells, malignant cell exhibited the most active levels of risk score by ssGSEA. Accordingly, intercellular communications including LT, CD70, CD40 and SPP1, and the like, between high-risk and low-risk were explored by the R package “Cellchat”.

Conclusion

We have constructed a model based on immunity of metastatic NPC and determined its prognostic value. The model identified the level of immune cell infiltration, cell-cell communication, along with potential immunotherapy for metastatic NPC.

A novel prognostic related lncRNA signature associated with amino acid metabolism in glioma

Background

Glioma is one of the deadliest malignant brain tumors in adults, which is highly invasive and has a poor prognosis, and long non-coding RNAs (lncRNAs) have key roles in the progression of glioma. Amino acid metabolism reprogramming is an emerging hallmark in cancer. However, the diverse amino acid metabolism programs and prognostic value remain unclear during glioma progression. Thus, we aim to find potential amino-related prognostic glioma hub genes, elaborate and verify their functions, and explore further their impact on glioma.

Methods

Glioblastoma (GBM) and low-grade glioma (LGG) patients' data were downloaded from TCGA and CCGA datasets. LncRNAs associated with amino acid metabolism were discriminated against via correlation analysis. LASSO analysis and Cox regression analysis were conducted to identify lncRNAs related to prognosis. GSVA and GSEA were performed to predict the potential biological functions of lncRNA. Somatic mutation data and CNV data were further built to demonstrate genomic alterations and the correlation between risk scores. Human glioma cell lines U251 and U87-MG were used for further validation in vitro experiments.

Results

There were eight amino-related lncRNAs in total with a high prognostic value that were identified via Cox regression and LASSO regression analyses. The high risk-score group presented a significantly poorer prognosis compared with the low risk-score group, with more clinicopathological features and characteristic genomic aberrations. Our results provided new insights into biological functions in the above signature lncRNAs, which participate in the amino acid metabolism of glioma. LINC01561 is one of the eight identified lncRNAs, which was adopted for further verification. In in vitro experiments, siRNA-mediated LINC01561 silencing suppresses glioma cells' viability, migration, and proliferation.

Conclusion

Novel amino-related lncRNAs associated with the survival of glioma patients were identified, and a lncRNA signature can predict glioma prognosis and therapy response, which possibly has vital roles in glioma. Meanwhile, it emphasized the importance of amino acid metabolism in glioma, particularly in providing deeper research at the molecular level.

Long Noncoding RNA MIR600HG Binds to MicroRNA-125a-5p to Prevent Pancreatic Cancer Progression Via Mitochondrial Tumor Suppressor 1-Dependent Suppression of Extracellular Regulated Protein Kinases Signaling Pathway

OBJECTIVES

Significance of long noncoding RNAs in pancreatic cancer (PC) progression has been documented. Here, we identified a novel long noncoding RNA MIR600HG in PC and its underlying mechanism during PC progression.

METHODS

Through bioinformatics analysis, we selected MIR600HG, microRNA-125a-5p (miR-125a-5p), and mitochondrial tumor suppressor 1 (MTUS1) as objects with their expression patterns assayed in the collected PC tissues and PC cells. Pancreatic cancer cells were manipulated with ectopic expression and deficiency of MIR600HG, miR-125a-5p, and/or MTUS1 for assaying cell biological processes in vitro and tumorigenesis in vivo.

RESULTS

MIR600HG and MTUS1 levels were downregulated and miR-125a-5p was upregulated in PC tissues and cells. MIR600HG could bind to miR-125a-5p, while miR-125a-5p negatively targeted MTUS1. MIR600HG resulted in suppression in malignant properties of PCs. All these changes could be reversed by miR-125a-5p elevation. In addition, miR-125a-5p targeted MTUS1 to activate the extracellular regulated protein kinases signaling pathway. In vivo experiment also verified the inhibitory role of MIR600HG in PC.

CONCLUSIONS

Taken together, MIR600HG acts as an inhibitor for PC progression by upregulating miR-125a-5p-mediated MTUS1 through extracellular regulated protein kinases pathway.

Identification and validation of a T-cell-related MIR600HG/hsa-mir-21-5p competing endogenous RNA network in tuberculosis activation based on integrated bioinformatics approaches

Background: T cells play critical roles in the progression of tuberculosis (TB); however, knowledge regarding these molecular mechanisms remains inadequate. This study constructed a critical ceRNA network was constructed to identify the potentially important role of TB activation via T-cell regulation.

Methods: We performed integrated bioinformatics analysis in a randomly selected training set from the GSE37250 dataset. After estimating the abundance of 18 types of T cells using ImmuCellAI, critical T-cell subsets were determined by their diagnostic accuracy in distinguishing active from latent TB. We then identified the critical genes associated with T-cell subsets in TB activation through co-expression analysis and PPI network prediction. Then, the ceRNA network was constructed based on RNA complementarity detection on the DIANA-LncBase and mirDIP platform. The gene biomarkers included in the ceRNA network were lncRNA, miRNA, and targeting mRNA. We then applied an elastic net regression model to develop a diagnostic classifier to assess the significance of the gene biomarkers in clinical applications. Internal and external validations were performed to assess the repeatability and generalizability.

Results: We identified CD4⁺ T, Tr1, nTreg, iTreg, and Tfh as T cells critical for TB activation. A ceRNA network mediated by the MIR600HG/hsa-mir-21-5p axis was constructed, in which the significant gene cluster regulated the critical T subsets in TB activation. MIR600HG, hsa-mir-21-5p, and five targeting mRNAs (BCL11B, ETS1, EPHA4, KLF12, and KMT2A) were identified as gene biomarkers. The elastic net diagnostic classifier accurately distinguished active TB from latent. The validation analysis confirmed that our findings had high generalizability in different host background cases.

Conclusion: The findings of this study provided novel insight into the underlying mechanisms of TB activation and identifying prospective biomarkers for clinical applications.

Identification of genomic instability related lncRNA signature with prognostic value and its role in cancer immunotherapy in pancreatic cancer

Background: Increasing evidence suggested the critical roles of lncRNAs in the maintenance of genomic stability. However, the identification of genomic instability-related lncRNA signature (GILncSig) and its role in pancreatic cancer (PC) remains largely unexplored.

Methods: In the present study, a systematic analysis of lncRNA expression profiles and somatic mutation profiles was performed in PC patients from The Cancer Genome Atlas (TCGA). We then develop a risk score model to describe the characteristics of the model and verify its prediction accuracy. ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and CIBERSORT analysis were employed to reveal the correlation between tumor immune microenvironment, immune infiltration, immune checkpoint blockade (ICB) therapy, and GILncSig in PC.

Results: We identified 206 GILnc, of which five were screened to develop a prognostic GInLncSig model. Multivariate Cox regression analysis and stratified analysis revealed that the prognostic value of the GILncSig was independent of other clinical variables. Receiver operating characteristic (ROC) analysis suggested that GILncSig is better than the existing lncRNA-related signatures in predicting survival. Additionally, the prognostic performance of the GILncSig was also found to be favorable in patients carrying wild-type KRAS, TP53, and SMAD4. Besides, a nomogram exhibited appreciable reliability for clinical application in predicting the prognosis of patients. Finally, the relationship between the GInLncSig model and the immune landscape in PC reflected its application value in clinical immunotherapy.

Conclusion: In summary, the GILncSig identified by us may serve as novel prognostic biomarkers, and could have a crucial role in immunotherapy decisions for PC patients.

A 70‑RNA model based on SVR and RFE for predicting the pancreatic cancer clinical prognosis

Researches on the prognosis of pancreatic cancer is of great significance to improve the patient treatment effect and survival. Current researches mainly focus on the prediction of the survival status and the determination of prognostic markers. Each patient has its own characteristics, there is no report about the prediction of survival time. However, accurate prediction of survival time is critical for personalized medicine. In this paper, a hybrid algorithm of Support Vector Regression (SVR) and Recursive Feature Elimination (RFE) was used to construct a quantitative prediction model of Overall Survival (OS) for pancreatic cancer patients, 70 RNAs related to OS were determined, including 33 mRNAs, 28 lncRNAs, and 9 miRNAs. The results of 10-fold cross-validation (R2 is 0.9693) and the generalization ability (R2 is 0.9666) showed that the model has reliable predictive performance and these 70 RNAs are important factors influencing the OS of pancreatic cancer patients. To further study the relationship between RNA-RNA interaction and the survival, competitive endogenous RNA (ceRNA) regulation network was constructed. Degree centrality, betweenness centrality and closeness centrality of nodes in the ceRNA network showed that hsa-mir-570, hsa-mir-944, hsa-mir-6506, hsa-mir-3136, MMP16, PLGLB2, HPGD, FUT1, MFSD2A, SULT1E1, SLC13A5, ZNF488, F2RL2, TNFRSF8, TNFSF11, FHDC1, ISLR2 and THSD7B are hub nodes, which are key RNAs closely determining the OS of pancreatic cancer patients.

LncRNA C9orf139 can regulate the progression of esophageal squamous carcinoma by mediating the miR-661/HDAC11 axis

•

LncRNA C9orf139 was highly expressed in ESCC.

•

LncRNA C9orf139 could negatively regulate miR-661 expression.

•

HDAC11 expression was negatively regulated by miR-661.

•

LncRNA C9orf139 regulates the progression of ESCC through the miR-661/HDAC11 axis.

Increasing evidence has indicated that long non-coding RNAs (LncRNAs) play multiple functions in the development of cancer and function as indicators of diagnosis and prognosis. This aim of this study was to investigate the roles LncRNA C9orF139 had in the progression of esophageal squamous carcinoma (ESCC). We found C9orf139 was highly expressed in ESCC and knock down the expression of C9orf139 significantly suppressed cell proliferation, promoted apoptosis, and inhibited migration and invasion. C9orf139 was able to negatively regulate miR-661 expression. At the same time, HDAC11 expression was negatively regulated by miR-661. The C9orf139/miR-661/HDAC11 axis was further involved in regulating the expression of the NF-κB signaling pathway. The association between the C9orf139 knockdown and the reduced tumor growth and size was observed during in vivo study. C9orf139 is highly expressed in ESCC, and is thus qualified to be used as a potential diagnostic and prognostic marker for ESCC. Its promotion of ESCC progression is achieved by mediating the miR-661/HDAC11 axis.

MIR600HG sponges miR-125a-5p to regulate glycometabolism and cisplatin resistance of oral squamous cell carcinoma cells via mediating RNF44

There is increasing evidence that dysregulated long non-coding RNA (lncRNA) is implicated in tumorigenesis and progression. We aim to explore the role of lncRNA MIR600HG in glycometabolism and cisplatin (DDP) resistance of oral squamous cell carcinoma (OSCC) cells via regulating microRNA-125a-5p (miR-125a-5p) and RING finger 44 (RNF44). Expression of MIR600HG, miR-125a-5p, and RNF44 in OSCC clinical samples, cell lines, and DDP-resistant OSCC cells (SCC-9/DDP) was determined. In SCC-9 cells, proliferation, IC50 value of DDP, migration, invasion, and apoptosis were detected; in SCC-9/DDP cells, proliferation, IC50 value of DDP, apoptosis, glucose consumption, and production of lactic acid and ATP were evaluated. The interaction of MR600HG, miR-125a-5p, and RNF44 was verified. MIR600HG and RNF44 were upregulated while miR-125a-5p was downregulated in OSCC tissues and cell lines, and also in SCC-9/DDP cells. In SCC-9 cells, MIR600HG overexpression improved cell growth, metastasis, and inhibited cell susceptibility to DDP; in SCC-9/DDP cells, silencing of MIR600HG promoted apoptosis, improved DDP sensitivity, and inhibited cell glycolysis. Downregulation of miR-125a-5p showed the opposite effect to downregulation of MIR600HG. MIR600HG bound to miR-125a-5p and miR-125a-5p targeted RNF44. Downregulation of miR-125a-5p reversed the improvement of DDP sensitivity and the inhibition of cell glycolysis by downregulated MIR600HG on SCC-9/DDP cells. Downregulating RNF44 reversed the promotion of DDP resistance and cell glycolysis of SCC-9/DDP cells mediated by downregulation of miR-125a-5p. Collectively, our study addresses that MIR600HG downregulation elevates miR-125a-5p and reduces RNF44 expression, thereby improving DDP sensitivity and inhibiting glycolysis in DDP-resistant OSCC cells.

Robust Validation and Comprehensive Analysis of a Novel Signature Derived from Crucial Metabolic Pathways of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a dismal prognosis. PDAC have extensively reprogrammed metabolic characteristics influenced by interactions with normal cells, the effects of the tumor microenvironment and oncogene-mediated cell-autonomous pathways. In this study, we found that among all cancer hallmarks, metabolism played an important role in PDAC. Subsequently, a 16-gene prognostic signature was established with genes derived from crucial metabolic pathways, including glycolysis, bile acid metabolism, cholesterol homeostasis and xenobiotic metabolism (gbcx). The signature was used to distinguish overall survival in multiple cohorts from public datasets as well as a validation cohort followed up by us at Shanghai Cancer Center. Notably, the gbcx-related risk score (gbcxMRS) also accurately predicted poor PDAC subtypes, such as pure-basal-like and squamous types. At the same time, it also predicted PDAC recurrence. The gbcxMRS was also associated with immune cells, especially CD8 T cells, Treg cells. Furthermore, a high gbcxMRS may indicate high drug sensitivity to irinotecan and docetaxel and CTLA4 inhibitor immunotherapy. Taken together, these results indicate a robust and reproducible metabolic-related signature based on analysis of the overall pathogenesis of pancreatic cancer, which may have excellent prognostic and therapeutic implications for PDAC.

Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

Background

Pyroptosis is regulated by long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of pyroptosis-related lncRNAs (PRLs) in OC is crucial for developing therapeutic strategies and survival prediction.

Methods

Based on public database raw data, mutations in the landscape of pyroptosis-related genes (PRGs) in patients with OC were investigated thoroughly. PRLs were identified by calculating Pearson correlation coefficients. Cox and LASSO regression analyses were performed on PRLs to screen for lncRNAs participating in the risk signature. Furthermore, receiver operating characteristic (ROC) curves, Kaplan-Meier survival analyses, decision curve analysis (DCA) curves, and calibration curves were used to confirm the clinical benefits. To assess the ability of the risk signature to independently predict prognosis, it was included in a Cox regression analysis with clinicopathological parameters. Two nomograms were constructed to facilitate clinical application. In addition, potential biological functions of the risk signature were investigated using gene function annotation. Subsequently, immune-related landscapes and BRCA1/2 mutations were compared in different risk groups using diverse bioinformatics algorithms. Finally, we conducted a meta-analysis and in-vitro assays on alternative lncRNAs.

Results

A total of 374 patients with OC were randomized into training and validation cohorts (7:3). A total of 250 PRLs were selected from all the lncRNAs. Subsequently, a risk signature (DICER1-AS1, MIR600HG, AC083880.1, AC109322.1, AC007991.4, IL6R-AS1, AL365361.1, and AC022098.2) was constructed to distinguish the risk of patient survival. The ROC curve, K-M analysis, DCA curve, and calibration curve indicated excellent predictive performance for determining overall survival (OS) based on the risk signature in each cohort (p < 0.05). The Cox regression analysis indicated that the risk signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the immune response and BRCA1 mutations were identified in different groups distinguished by the risk signature (p < 0.05). Interestingly, in-vitro assays showed that an alternative lncRNA (DICER1-AS1) could promote OC cell proliferation.

Conclusion

The PRL risk signature could independently predict overall survival and guide treatment in patients with OC.

Identification of a Four Cancer Stem Cell-Related Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Adenocarcinoma

BACKGROUND

Cancer stem cells (CSCs) are now being considered as the initial component in the development of pancreatic adenocarcinoma (PAAD). Our aim was to develop a CSCrelated signature to assess the prognosis of PAAD patients for the optimization of treatment.

METHODS

Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue in the Cancer Genome Atlas (TCGA) were screened out, and the weighted gene correlation network analysis (WGCNA) was employed to identify the CSC-related gene sets. Then, univariate, Lasso Cox regression analyses and multivariate Cox regression were applied to construct a prognostic signature using the CSC-related genes. Its prognostic performance was validated in TCGA and ICGC cohorts. Furthermore, Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors in PAAD, and a prognostic nomogram was established.

RESULTS

The Kaplan-Meier analysis, ROC curve and C-index indicated the good performance of the CSC-related signature at predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression revealed that the CSC-related signature was an independent prognostic factor in PAAD. The nomogram was superior to the risk model and AJCC stage in predicting OS. In terms of mutation and tumor immunity, patients in the high-risk group had higher tumor mutation burden (TMB) scores than patients in the low-risk group, and the immune score and the ESTIMATE score were significantly lower in the high-risk group. Moreover, according to the results of principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA), the low-risk and high-risk groups displayed different stemness statuses based on the risk model.

CONCLUSION

Our study identified four CSC-related gene signatures and established a prognostic nomogram that reliably predicts OS in PAAD. The findings may support new ideas for screening therapeutic targets to inhibit stem characteristics and the development of PAAD.

lncRNA MIR600HG Knockdown Alleviates Cognitive Impairment in Alzheimer's Disease Through NEDD4L Mediated PINK1 Degradation

BACKGROUND

Growing evidence has demonstrated that long non-coding RNAs (lncRNAs) play a critical role in Alzheimer's disease (AD), which is characterized by sustained mitochondrial dysfunction, inevitable memory loss, and cognitive decline. However, the potential function of lncRNAs MIR600 Host Gene (MIR600HG) in AD remains unanswered.

OBJECTIVE

Our study aimed to investigate the role of MIR600HG and its related molecular mechanism in AD.

METHODS

The expression of MIR600HG was examined by qRT-PCR. The MIR600HG interacting proteins were identified by RNA pull-down assay and mass spectrometry and verified by RNA immunoprecipitation. Immunofluorescence staining was applied to examine the colocalization of PINK1 and NEDD4L. The PINK1 level and the activation of autophagy were detected by immunoblotting. Morris water maze test was performed to evaluate cognitive decline in AD mice model.

RESULTS

MIR600HG expression was elevated during aging in two different types of AD transgenic mouse models. Next, we found that increased MIR600HG directly interact with NEDD4L, which promoted PINK1 ubiquitination and degradation, and as well as autophagy activation. Additionally, MIR600HG promoted Aβ production and suppressed Cytochrome C Oxidase activity. Administration of AAV-shMIR600HG restored the Cytochrome C Oxidase activity and inhibited Aβ production. Furthermore, PINK1 overexpression or MIR600HG knockdown significantly ameliorated the cognitive impairment in APP/PS1 mice. PINK1 depletion recovered the spatial memory defect in the AAV-shMIR600HG injected APP/PS1 mice.

CONCLUSION

MIR600HG was increased in AD and promoted AD pathogenesis. Targeting MIR600HG significantly improved cognitive function in AD mice, which could pave the way for exciting new avenues in AD therapeutic strategy research.

Integrated Analysis of lncRNA-Associated ceRNA Network Identifies Two lncRNA Signatures as a Prognostic Biomarker in Gastric Cancer

Background

Gastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction.

Methods

The lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs.

Results

Differentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells.

Conclusions

The authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.

A prognostic score based on long-term survivor unique transcriptomic signatures predicts patient survival in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is known for its poor prognosis with few long-term survivors. This study aimed to establish a prognostic score using unique transcriptomic profiles of long-term survivors to be used as a patient selection tool for meaningful clinical intervention in PDAC. In TCGA PDAC cohort, 16 genes were significantly upregulated in the long-term survivor tumors. A prognostic score was established using these 16 genes by LASSO Cox regression, and PHKG1, HOXA4, ISL2, DMRT3 and TRA2A gene expressions were included in the score. The prognostic value was confirmed in both testing and validation cohorts. The characteristics of the high score tumor was investigated by bioinformatical approach. The high score tumor was associated with TP53 mutation but not with other commonly enhanced signaling pathways in PDAC. The high score tumor was associated with higher tumor mutational burden and unfavorable tumor microenvironment (TME), such as lower infiltration of CD8-positive T cells and dendritic cells, and less cell composition of mature blood vessels and fibroblasts. The high score tumor was also associated with enhanced cell proliferation and margin positivity after surgery. The impact of score component genes on the cell proliferation was investigated by in vitro experiments. Silencing of the score component genes promoted cell proliferation. In conclusion, the prognostic score predicted PDAC patient survival and was associated with cancer aggressiveness such as unfavorable TME and enhanced cell proliferation.

Construction of a novel risk model based on the random forest algorithm to distinguish pancreatic cancers with different prognoses and immune microenvironment features

Immune-related long noncoding RNAs (irlncRNAs) are actively involved in regulating the immune status. This study aimed to establish a risk model of irlncRNAs and further investigate the roles of irlncRNAs in predicting prognosis and the immune landscape in pancreatic cancer. The transcriptome profiles and clinical information of 176 pancreatic cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Immune-related genes (irgenes) downloaded from ImmPort were used to screen 1903 immune-related lncRNAs (irlncRNAs) using Pearson’s correlation analysis (R > 0.5; p < 0.001). Random survival forest (RSF) and survival tree analysis showed that 9 irlncRNAs were highly correlated with overall survival (OS) according to the variable importance (VIMP) and minimal depth. Next, Cox regression analysis was used to establish a risk model with 3 irlncRNAs (LINC00462, LINC01887, RP11-706C16.8) that was evaluated by Kaplan-Meier analysis, the areas under the curve (AUCs) of the receiver operating characteristics and the C-index. Additionally, we performed Cox regression analysis to establish the clinical prognostic model, which showed that the risk score was an independent prognostic factor (p < 0.001). A nomogram and calibration plots were drawn to visualize the clinical features. The Wilcoxon signed-rank test and Pearson’s correlation analysis further explored the irlncRNA signatures and immune cell infiltration, as well as the immunotherapy response.

Construction of a prognostic model with histone modification-related genes and identification of potential drugs in pancreatic cancer

Background

Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. An effective prediction model is urgently needed for the accurate assessment of patients’ prognosis to assist clinical decision-making.

Methods

Gene expression data and clinicopathological data of the samples were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differential expressed genes (DEGs) analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, random forest screening and multivariate Cox regression analysis were applied to construct the risk signature. The effectiveness and independence of the model were validated by time-dependent receiver operating characteristic (ROC) curve, Kaplan–Meier (KM) survival analysis and survival point graph in training set, test set, TCGA entire set and GSE57495 set. The validity of the core gene was verified by immunohistochemistry and our own independent cohort. Meanwhile, functional enrichment analysis of DEGs between the high and low risk groups revealed the potential biological pathways. Finally, CMap database and drug sensitivity assay were utilized to identify potential small molecular drugs as the risk model-related treatments for PC patients.

Results

Four histone modification-related genes were identified to establish the risk signature, including CBX8, CENPT, DPY30 and PADI1. The predictive performance of risk signature was validated in training set, test set, TCGA entire set and GSE57495 set, with the areas under ROC curve (AUCs) for 3-year survival were 0.773, 0.729, 0.775 and 0.770 respectively. Furthermore, KM survival analysis, univariate and multivariate Cox regression analysis proved it as an independent prognostic factor. Mechanically, functional enrichment analysis showed that the poor prognosis of high-risk population was related to the metabolic disorders caused by inadequate insulin secretion, which was fueled by neuroendocrine aberration. Lastly, a cluster of small molecule drugs were identified with significant potentiality in treating PC patients.

Conclusions

Based on a histone modification-related gene signature, our model can serve as a reliable prognosis assessment tool and help to optimize the treatment for PC patients. Meanwhile, a cluster of small molecule drugs were also identified with significant potentiality in treating PC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01928-6.

Long noncoding RNAs: role and contribution in pancreatic cancer

Noncoding RNAs are proclaimed to be expressed in various cancer types and one such type is found to be pancreatic ductal adenocarcinoma (PDAC). The long noncoding RNAs (LncRNAs) affect the migration, invasion, and growth of tumor cells by playing important roles in the process of epigenesis, post-transcription, and transcriptional regulation along with the maintenance of apoptosis and cell cycle. It is quite subtle whether the alterations in lncRNAs would impact PDAC progression and development. This review throws a spotlight on the lncRNAs associated with tumor functions: MALAT-1, HOTAIR, HOXA13, H19, LINC01559, LINC00460, SNHG14, SNHG16, DLX6-AS1, MSC-AS1, ABHD11-AS1, DUXAP8, DANCR, XIST, DLEU2, etc. are upregulated lncRNAs whereas GAS5, HMlincRNA717, MIAT, LINC01111, lncRNA KCNK15-AS1, etc. are downregulated lncRNAs inhibiting the invasion and progression of PDAC. These data provided helps in the assessment of lncRNAs in the development, metastasis, and occurrence of PDAC and also play a vital role in the evolution of biomarkers and therapeutic agents for the treatment of PDAC.

Exploration of the Associations of lncRNA Expression Patterns with Tumor Mutation Burden and Prognosis in Colon Cancer

Background

Tumor mutation burden (TMB) is emerging as a new biomarker to monitor the response of cancer patients to immunotherapy. Long non-coding RNAs (lncRNAs) are critical in regulating gene expression and play a significant role in cancer-associated immune responses. However, the association between lncRNA expression patterns and TMB levels and survival outcomes remains unknown in colon cancer.

Methods

In colon cancer patients from The Cancer Genome Atlas Program (TCGA), a multi-lncRNAs based classifier for predicting TMB levels was established using the least absolute shrinkage and selection operator (LASSO) method. The association between classifier index and immune-related characteristics of patients was also investigated. Quantitative polymerase chain reaction (qPCR) was used to verify the expression levels of these lncRNAs in normal and CRC cell lines.

Results

The multi-lncRNAs based classifier had ability to predict TMB level of patients with accuracy (AUC= 0.70), and the general applicability of this classifier was proved in the validation set (AUC= 0.71) and the pooled set (AUC= 0.70). The classifier index was related to three immune checkpoints (PD1, PD-L1, and CTLA-4), the infiltration level of immune cells, and immune response-related score (IFN-γ score, gene expression profiles (GEP) score, cytolytic activity (CYT) score and MHC score). A nomogram, which integrates classifier and some common clinical information, was able to predict the overall survival of colon cancer patients accurately.

Conclusion

LncRNA expression patterns are associated with TMB, which may serve as a classifier to predict the TMB in colon cancer patients. The nomogram could potentially evaluate survival outcomes and provide a reference to better manage colon cancer patients.

MIR600HG suppresses metastasis and enhances oxaliplatin chemosensitivity by targeting ALDH1A3 in colorectal cancer

Background: Metastasis and chemoresistance indicate a poor prognosis in colorectal cancer (CRC) patients. However, the mechanisms that lead to the development of chemoresistance and metastasis in CRC remain unclear.

Materials and methods: We combined clinical and experimental studies to determine the role of MIR600HG in CRC metastasis and chemoresistance. The statistical analysis was performed using GraphPad Prism software, version 8.0.

Results: We detected down-regulated expression of long non-coding RNA (lncRNA) MIR600HG in CRC specimens and cell lines compared with normal controls, and the expression level of MIR600HG was inversely correlated with the overall survival of CRC patients. The inhibition of MIR600HG stimulated CRC cell metastasis and chemoresistance. In addition, our data showed that the inhibition of MIR600HG stimulated CRC stemness, while the overexpression of MIR600HG suppressed stemness. Importantly, our animal experiments showed that MIR600HG inhibited tumour formation and that the combination of MIR600HG inhibition and oxaliplatin (Oxa) treatment significantly inhibited tumour growth compared with that with either intervention alone. Furthermore, we demonstrated that MIR600HG exerts its anticancer role by targeting ALDH1A3 in CRC.

Conclusions: Our data suggest that MIR600HG functions as a tumour suppressor and that the overexpression of MIR600HG inhibits tumour invasion and enhances chemosensitivity, providing a new strategy for CRC treatment.

Construction of Competitive Endogenous RNA Network and Verification of 3-Key LncRNA Signature Associated With Distant Metastasis and Poor Prognosis in Patients With Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common malignancy with high distant metastasis rate. Long non-coding RNAs (LncRNAs) are reported to be upregulated or downregulated in multiple cancers and play a crucial role in the metastasis of tumors or prognosis. Therefore, the purpose of our study is to construct a prognostic signature for ccRCC based on distant metastasis-related lncRNAs and explore the involved potential competitive endogenous RNA (ceRNA) network. The differentially expressed genes (DEGs) screened from the database of the cancer genome atlas (TCGA) were used to construct a co-expression network and identify the distant metastasis-related module by weighted gene co-expression network analysis (WGCNA). Key genes with metastatic and prognostic significance were identified through rigorous screening, including survival analysis, correlation analysis, and expression analyses in stage, grade, and distant metastasis, and were verified in the data set of gene expression omnibus (GEO) and the database from gene expression profiling interactive analysis (GEPIA). The potential upstream miRNAs and lncRNAs were predicted via five online databases and LncBase. Here, we constructed a ceRNA network of key genes that are significantly associated with the distant metastasis and prognosis of patients with ccRCC. The distant metastasis-related lncRNAs were used to construct a risk score model through the univariate, least absolute shrinkage selection operator (LASSO), and multivariate Cox regression analyses, and the patients were divided into high- and low-risk groups according to the median of the risk score. The Kaplan–Meier survival analysis demonstrated that mortality was significantly higher in the high-risk group than in the low-risk group. Considering the other clinical phenotype, the Cox regression analyses indicated that the lncRNAs model could function as an independent prognostic factor. Quantitative real-time (qRT)-PCR in the tissues and cells of ccRCC verified the high-expression level of three lncRNAs. Gene set enrichment analysis (GSEA) revealed that the lncRNA prognostic signature was mainly enriched in autophagy- and immune-related pathways, indicating that the autophagy and immune functions may play an important role in the distant metastasis of ccRCC. In summary, the constructed distant metastasis-related lncRNA signature could independently predict prognosis in patients with ccRCC, and the related ceRNA network provided a new sight on the potential mechanism of distant metastasis and a promising therapeutic target for ccRCC.

A new 7-gene survival score assay for pancreatic cancer patient prognosis prediction

Gene expression features that are valuable for pancreatic ductal adenocarcinoma (PDAC) prognosis are still largely unknown. We aimed to explore pivotal molecular signatures for PDAC progression and establish an efficient survival score to predict PDAC prognosis. Overall, 163 overlapping genes were identified from three statistical methods, including differentially expressed genes (DEGs), coexpression network analysis (WGCNA), and target genes for miRNAs that were significantly related to PDAC patients' overall survival (OS). Then, according to the optimal value of the cross-validation curve (lambda = 0.031), 7 non-zero coefficients (ARNTL2, DSG3, PTPRR, ANLN, S100A14, ANKRD22, and TSPAN7) were selected to establish a prognostic prediction model of PDAC patients. We further confirmed the expression level of 7 genes using RT-PCR, western blot, and immunohistochemistry staining in PDAC patients' tissues. Our results showed that the ROC curve of the 7-mRNA model indicated good predictive ability for 1- and 2-year OS in three datasets (TCGA: 0.71, 0.69; ICGC: 0.8, 0.74; GEO batch: 0.61, 0.7, respectively). The hazard ratio (HR) of the low-risk group had a similar significant result (TCGA: HR = 0.3723; ICGC: HR = 0.2813; GEO batch: HR = 0.4999; all P < 0.001). Furthermore, Log-rank test results in three cohorts showed that the 7-mRNA assay excellently predicted the prognosis and metastasis, especially in TNM stage I&II subgroups of PDAC. In conclusion, the strong validation of our 7-mRNA signature indicates the promising effectiveness of its clinical application, especially in patients with TNM stages I&II.

A novel methylation signature predicts inferior outcome of patients with PDAC

Pancreatic ductal adenocarcinoma (PDAC) will become the second most common cause of death in North America and Europe over the next 10 years owing to the lack of early diagnosis, poor treatment, and poor prognosis. This study evaluated the methylation array data of 184 patients with PDAC in The Cancer Genome Atlas database to explore methylation biomarkers related to patient outcome. Using Univariable Cox regression analysis and Lasso regression analysis method in the training dataset, it was found that the four DNA methylation markers (CCNT1, ITGB3, SDS, and HMOX2) were significantly correlated with the overall survival of patients with PDAC. Kaplan-Meier analysis showed that these four DNA methylation markers could significantly distinguish high-risk and low-risk patients. Receiver operating characteristic analysis further confirmed that the four DNA methylation markers had high sensitivity and specificity, which could predict the prognosis of patients. Moreover, there was a difference in the genetic mutations between high-risk and low-risk patients distinguished by the four-DNA methylation model, which can provide information for clinical treatment. Finally, compared with known biomarkers, the model was more accurate in predicting the prognosis of PDAC. This four-DNA methylation model has potential as a new independent prognostic indicator, and could be used for the diagnosis, monitoring, and precision medicine of pancreatic cancer.

Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high‑ and low‑risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.

LncRNA C9orf139 can regulate the growth of pancreatic cancer by mediating the miR-663a/Sox12 axis

BACKGROUND

Recent studies have proved the important role of many oncogenic long non-coding RNAs (lncRNAs) in the progression of pancreatic cancer, but little is known about the mechanisms of tumor suppression in pancreatic cancer.

AIM

To evaluate the function of tumor suppressor lncRNA C9orf139 in pancreatic cancer progression and to study the underlying mechanism.

METHODS

We assigned 54 patients with pancreatic ductal adenocarcinoma treated at our hospital to the patient group and 30 normal subjects undergoing physical examination to the control group. RT-qPCR was used to measure the relative expression of C9orf139 in the tissue and serum of patients, in an attempt to investigate the prognostic value of C9orf139 in pancreatic cancer patients. The luciferase reporter gene assay was performed to determine the interaction between C9orf139 and miR-663a. The biological function of C9orf139 was assessed by in vitro assays and in vivo subcutaneous tumor formation tests in animal models. To figure out the molecular mechanism of C9orf139 to act on miR-663a/Sox12, RNA pull-down, Western blot assay, RNA immunoprecipitation assay, and co-immunoprecipitation assay were performed.

RESULTS

C9orf139 level significantly increased in the tissue and serum of patients, which had clinical diagnostic value for pancreatic cancer. Patients with high C9orf139 expression had a higher risk of progressing to stage III + IV, lymph node metastasis, and poor differentiation. Cox regression analysis suggested that C9orf139, tumor-node-metastasis stage, and lymph node metastasis were independent prognostic factors in patients. The underlying mechanism of C9orf139 was that it promoted the growth of pancreatic cancer cells by modulating the miR-663a/Sox12 axis.

CONCLUSION

C9orf139 is highly expressed in pancreatic cancer, qualified to be used as a potential diagnostic and prognostic marker for pancreatic cancer. Its promotion of pancreatic cancer cell growth is achieved by mediating the miR-663a/Sox12 axis.

Prognostic significance of an autophagy-related long non-coding RNA signature in patients with oral and oropharyngeal squamous cell carcinoma

Traditional clinicopathological indices are insufficient in predicting the prognosis of patients diagnosed with oral and oropharyngeal squamous cell carcinoma (OSCC/OPSCC). Notably, autophagy and long non-coding RNAs (lncRNAs) regulate the development and progression of various types of cancer. The present study aimed to assess the association between autophagy-related lncRNAs and the prognosis of patients diagnosed with OSCC/OPSCC. Gene sequencing and clinicopathological data of patients with OSCC/OPSCC were downloaded from The Cancer Genome Atlas database, while gene set functional classification was downloaded from the Gene Set Enrichment Analysis database. Out of the 413 transcriptome data samples and 402 clinicopathological data samples retrieved, a total of nine autophagy-related lncRNAs, including PTCSC2, AC099850.3, LINC01963, RTCA-AS1, AP002884.1, UBAC2-AS1, AL512274.1, MIR600HG and AL354733.3, were screened. This was geared towards establishing a signature through gene co-expression network, univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses. Based on this signature, the patients were subdivided into a high-risk group and a low-risk group. Kaplan-Meier survival analysis revealed that the overall survival of the high-risk group was significantly lower than that of the low-risk group. Furthermore, principal components analysis demonstrated that the patients diagnosed with OSCC/OPSCC could be distinguished into low-survival and high-survival groups according to the signature. Univariate and multivariate Cox regression analyses of clinicopathological data and the signature revealed that the signature could potentially be used as an independent prognostic factor for OSCC/OPSCC. In addition, reverse transcription-quantitative PCR analysis of clinical samples demonstrated the validity of the signature. In summary, the present study revealed that the signature based on autophagy-related lncRNAs potentially acts as an independent prognostic indicator for patients with OSCC/OPSCC. Furthermore, it promotes research on targeted diagnosis and treatment of patients diagnosed with OSCC/OPSCC.

Weighted gene correlation network analysis identifies microenvironment-related genes signature as prognostic candidate for Grade II/III glioma

Glioma is the most common malignant tumor in the central nervous system. Evidence shows that clinical efficacy of immunotherapy is closely related to the tumor microenvironment. This study aims to establish a microenvironment-related genes (MRGs) model to predict the prognosis of patients with Grade II/III gliomas. Gene expression profile and clinical data of 459 patients with Grade II/III gliomas were extracted from The Cancer Genome Atlas. Then according to the immune/stromal scores generated by the ESTIMATE algorithm, the patients were scored one by one. Weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network to identify potential biomarkers for predicting the prognosis of patients. When adjusting clinical features including age, histology, grading, IDH status, we found that these features were independently associated with survival. The predicted value of the prognostic model was then verified in 440 samples in CGGA part B dataset and 182 samples in CGGA part C dataset by univariate and multivariate cox analysis. The clinical samples of 10 patients further confirmed our signature. Our findings suggested the eight-MRGs signature identified in this study are valuable prognostic predictors for patients with Grade II/III glioma.

A two-gene-based prognostic signature for pancreatic cancer

The purpose of this study was to identify a vital gene signature that has prognostic value for pancreatic cancer based on gene expression datasets from the Cancer Genome Atlas and Gene Expression Omnibus. A total of 34 genes were obtained by the univariate analysis, which were significantly associated with the overall survival of PC patients. After further analysis, Anillin (ANLN) and Histone H1c (HIST1H1C) were identified and considered to be the most significant prognostic genes among the 34 genes. A prognostic model based on these two genes was constructed, and successfully distinguished pancreatic cancer survival into high-risk and low-risk groups in the training set and testing set. Subsequently, independent predictive factors, including the age, margin condition and risk score, were then employed to construct the nomogram model. The area under curve for the nomogram model was 0.826 at 0.5 years and 0.726 at 1 year, and the C-index of the nomogram model was 0.664 higher than the others variables alone. These findings have indicated that high expression of ANLN and HIST1H1C predicted poor outcomes for patients with pancreatic cancer. The nomogram model based on the expression of two genes could be valuable for the guidance of clinical treatment.

Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

Background

Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed.

Methods

Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC.

Results

Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways.

Conclusion

Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.

Research progress on long non-coding RNAs and their roles as potential biomarkers for diagnosis and prognosis in pancreatic cancer

Pancreatic cancer is one of the main causes of tumor-related deaths worldwide because of its low morbidity but extremely high mortality, and is therefore colloquially known as the "king of cancer." Sudden onset and lack of early diagnostic biomarkers directly contribute to the extremely high mortality rate of pancreatic cancer patients, and also make it indistinguishable from benign pancreatic diseases and precancerous pancreatic lesions. Additionally, the lack of effective prognostic biomarkers makes it difficult for clinicians to formulate precise follow-up strategies based on the postoperative characteristics of the patients, which results in missed early diagnosis of recurrent pancreatic cancer. Long non-coding RNAs (lncRNAs) can influence cell proliferation, invasion/migration, apoptosis, and even chemoresistance via regulation of various signaling pathways, leading to pro- or anti-cancer outcomes. Given the versatile effects of lncRNAs on tumor progression, using a single lncRNA or combination of several lncRNAs may be an effective method for tumor diagnosis and prognostic predictions. This review will give a comprehensive overview of the most recent research related to lncRNAs in pancreatic cancer progression, as targeted therapies, and as biomarkers for the diagnosis and prognosis of pancreatic cancer.

The implication of long non-coding RNAs in the diagnosis, pathogenesis and drug resistance of pancreatic ductal adenocarcinoma and their possible therapeutic potential

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies with the lowest median and overall survival rate among all human malignancies. The major problems with the PDAC are the late diagnosis, metastasis, and acquired resistance to chemotherapeutic agents in the clinic. Over the last decade, the long non-coding RNAs (lncRNAs) have been discovered and occupies a significantly large proportion of the human genome. Recent studies have proved that lncRNAs can play a crucial role in the majority of key cellular processes involved in the maintenance of cellular homeostasis by regulating various molecular mechanisms. The deregulation of lncRNAs has been associated with various chronic diseases including human malignancies. Several lncRNAs have tumor-specific expression making them an ideal and excellent target for designing the novel therapeutic strategies against human malignancies. We have discussed how lncRNA expression can be used for the diagnosis and prognosis of PDAC. The current review discusses the potential role and molecular mechanism of lncRNA in regulating the prominent hallmarks of cancer including abnormal growth, survival, metastasis, and drug-resistance in PDAC. Importantly, we also highlight the possible application of various therapeutic strategies including small interfering RNA, CRISPR-Cas9, antisense oligonucleotides, locked nucleic acid Gapmers, small molecules, aptamers, lncRNA promoter to target the lncRNA as a novel and viable options for treatment of PDAC.

Comprehensive analysis of a long non-coding RNA-associated competing endogenous RNA network in glioma

Long non-coding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs), interacting with microRNAs (miRNAs) and playing an important role in tumor progression. However, the role of lncRNA-mediated ceRNAs in glioma remains largely unknown. The present study aimed to identify novel lncRNAs and their associated function in glioma. RNA sequencing and corresponding clinical data from patients with glioma were obtained from The Cancer Genome Atlas. A total of 598 glioma tissues and 5 normal brain tissues were analyzed in the present study. The differentially expressed (DE) lncRNAs, mRNAs and miRNAs were identified using R packages and were used to construct a ceRNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate the biological functions of the DEmRNAs. Kaplan-Meier curve analysis was performed to investigate the association between DElncRNA expression and patient outcome. A total of 752 DElncRNAs, 2,079 DEmRNAs and 113 DEmiRNAs were identified between glioma and normal tissues. A lncRNA-miRNA-mRNA ceRNA network consisting of 61 lncRNAs, 12 miRNAs and 92 mRNAs was constructed. Survival analysis indicated that 36 DElncRNAs, 72 DEmRNAs and 3 DEmiRNAs were associated with overall survival in patients with glioma. The present study identified novel lncRNAs associated with survival prognosis and may facilitate further investigation of lncRNA-mediated ceRNA regulatory mechanisms in glioma.

Comprehensive Analysis of LincRNAs in Classical and Basal-Like Subtypes of Pancreatic Cancer

Pancreatic ductal adenocarcinomas (PDAC) belong to the deadliest malignancies in the western world. Mutations in TP53 and KRAS genes along with some other frequent polymorphisms occur almost universally and are major drivers of tumour initiation. However, these mutations cannot explain the heterogeneity in therapeutic responses and differences in overall survival observed in PDAC patients. Thus, recent classifications of PDAC tumour samples have leveraged transcriptome-wide gene expression data to account for epigenetic, transcriptional and post-transcriptional mechanisms that may contribute to this deadly disease. Intriguingly, long intervening RNAs (lincRNAs) are a special class of long non-coding RNAs (lncRNAs) that can control gene expression programs on multiple levels thereby contributing to cancer progression. However, their subtype-specific expression and function as well as molecular interactions in PDAC are not fully understood yet. In this study, we systematically investigated the expression of lincRNAs in pancreatic cancer and its molecular subtypes using publicly available data from large-scale studies. We identified 27 deregulated lincRNAs that showed a significant different expression pattern in PDAC subtypes suggesting context-dependent roles. We further analyzed these lincRNAs regarding their common expression patterns. Moreover, we inferred clues on their functions based on correlation analyses and predicted interactions with RNA-binding proteins, microRNAs, and mRNAs. In summary, we identified several PDAC-associated lincRNAs of prognostic relevance and potential context-dependent functions and molecular interactions. Hence, our study provides a valuable resource for future investigations to decipher the role of lincRNAs in pancreatic cancer.

Relationship between a 7-mRNA signature of the pancreatic adenocarcinoma microenvironment and patient prognosis (a STROBE-compliant article)

The potential association between the prognosis of the pancreatic adenocarcinoma (PAAD) and its microenvironment is unclear. This study aims to construct a prognostic index (PI) model of the PAAD microenvironment to predict PAAD patient survival outcomes.The mRNA sequencing and the clinical parameters data were obtained from The Cancer Genome Atlas. Immune and stromal scores were computed using the expression data algorithm to capture infiltration of immune and stromal cells in the PAAD tissue, where patients were categorized as high and low score groups according to these scores. Differentially expressed genes were identified using the R package LIMMA. Univariate and multivariate Cox regression analysis were conducted to select candidate survival-correlated gene signatures from the tumor microenvironment for constructing a model. The Kaplan-Meier method was used to access overall survival of the primary and validation cohorts. The immunological features of the PI model was explored using the Tumor Immune Estimation Resource (TIMER) database. Bioinformatic analyses were conducted based on the DAVID database.A total of 1266 overlapping differentially expressed genes and 49 prognosis-associated genes were identified. A 7-mRNA signature (GBP5, BICC1, SLC7A14, CYSLTR1, P2RY6, VENTX, and RAB39B) was screened for the construction of a PI model (area under the curve = 0.791). In both the primary and validation cohorts, Kaplan Meier analysis revealed that the overall survival of the high-risk group was significantly worse compared to the low-risk group (P < .0001, P = .0028 respectively). The TIMER database described that the 7 signature genes were correlated with immune infiltrating cells and tumor purity. Bioinformatic analyses revealed that these prognosis-associated genes were significantly enriched during inflammation, the defense response, would response, calcium ion transport, and plasma membrane part.A list of the prognosis-correlated genes was generated based on the PAAD microenvironment. A 7-mRNA PI model may be used for predicting the prognosis of PAAD patients.

The benefits of smoking cessation on survival in cancer patients by integrative analysis of multi-omics data

Few studies have examined the association between smoking status (including former smokers) at diagnosis and overall survival among cancer patients. We aimed to assess the benefits of quitting smoking on cancer prognosis in cohorts of cancer patient smokers obtained from the Cancer Genome Atlas (TCGA) database. Hazard ratios (HR) were calculated to evaluate smoking behavior at cancer diagnosis (reformed smokers vs. current smokers) in association with overall survival using multivariate‐adjusted Cox regressions analysis. According to our analyses, quitting smoking was the independent protective factor for overall survival in lung squamous cell carcinoma (LUSC) (HR = 0.67, 95% CI = 0.48–0.94). Comprehensive analysis of multicomponent data across reformed and current smokers identified a total of 85 differential expressed genes (DEGs) affected by different modes of genetic and epigenetic regulation, potentially representing cancer drivers in smokers. Moreover, we provided a smoking‐associated gene expression signature, which could evaluate the true effect on prognosis with high power (HR = 1.70, 95% CI = 1.19–2.43, AUC = 0.65, 0.67, and 0.70 for 2‐, 3‐, and 5‐year survival, respectively). This signature was also applicable in other smoking‐related cancers, including bladder urothelial carcinoma (HR = 1.70, 95% CI = 1.01–2.88), cervical carcinoma (HR = 5.69, 95% CI = 1.37–23.69), head and neck squamous cell carcinoma (HR = 1.97, 95% CI = 1.41–2.76), lung adenocarcinoma (HR = 1.73, 95% CI = 1.16–2.57), and pancreatic adenocarcinoma (HR = 4.28, 95% CI = 1.47–12.47). In conclusion, this study demonstrates that quitting smoking at diagnosis decreases risk of death in cancer patients. We also provide a smoking‐associated gene expression signature to evaluate the effect of smoking on survival. Lastly, we suggest that smoking cessation could comprise a part of cancer treatment to improve survival rates of cancer patients.

Construction of a Competitive Endogenous RNA Network for Pancreatic Adenocarcinoma Based on Weighted Gene Co-expression Network Analysis and a Prognosis Model

Pancreatic adenocarcinoma (PAAD) is a pancreatic disease with considerable mortality worldwide. Because of a lack of obvious symptoms at the early stage, most PAAD patients are diagnosed at the terminal stage and prognosis is usually poor. In this study, we firstly obtained RNA sequencing data of 181 patients with PAAD from The Cancer Genome Atlas (TCGA) database to identify early diagnostic biomarkers for PAAD. Survival-related mRNAs were identified using a weighted gene co-expression network analysis (WGCNA), and then a linear prognostic model of seven long non-coding RNAs (lncRNAs) was established using univariate and multivariate Cox proportional hazards regression analyses, which is verified using a time-dependent receiver operating characteristic (ROC) curve analysis. Finally, according to the survival analysis, we constructed a survival-related competing endogenous RNA (ceRNA) network. Our results showed that: (1) The upregulated genes related to cell cycle-related pathway (including homologous recombination, DNA replication and mismatch repair) in PAAD can increase the proliferation ability of cancer cells; (2) The 7-lncRNA signature can predict the overall survival (OS) of PAAD patients; and (3) The key mRNAs and lncRNAs are involved in mutual regulation in the ceRNA network.

Identification of a Seven-lncRNA Immune Risk Signature and Construction of a Predictive Nomogram for Lung Adenocarcinoma

Background

The incidence of lung cancer is the highest of all cancers, and it has the highest death rate. Lung adenocarcinoma (LUAD) is a major type of lung cancer. This study is aimed at identifying the prognostic value of immune-related long noncoding RNAs (lncRNAs) in LUAD.

Materials and Methods

Gene expression profiles and the corresponding clinicopathological features of LUAD patients were obtained from The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was performed on the prognostic immune-related lncRNAs to calculate the risk scores, and a risk signature was constructed. Survival analysis was performed to assess the prognostic value of the risk signature. A nomogram was also constructed based on the clinicopathological features and risk signature.

Results

A total of 437 LUAD patients with gene expression data and clinicopathological features were obtained in this study, which was considered the combination set. They were randomly and equally divided into a training set and a validation set. Seven immune-related lncRNAs (AC092794.1, AL034397.3, AC069023.1, AP000695.1, AC091057.1, HLA-DQB1-AS1, and HSPC324) were identified and used to construct a risk signature. The patients were divided into the low- and high-risk groups based on the median risk score of -0.04074. Survival analysis suggested that patients in the low-risk group had a longer overall survival (OS) than those in the high-risk group (p = 1.478e − 02). A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients (C-index of the nomogram was 0.755, and the AUCs for the 1-, 3-, and 5-year survivals were 0.826, 0.719, and 0.724, respectively). The validation and combination sets confirmed these results.

Conclusion

Our study identified seven novel immune-related lncRNAs and generated a risk signature, as well as a nomogram, that could predict the prognosis of LUAD patients.

A 17-gene expression-based prognostic signature associated with the prognosis of patients with breast cancer: A STROBE-compliant study

Identification of reliable predictive biomarkers for patients with breast cancer (BC).Univariate Cox proportional hazards regression model was conducted to identify genes correlated with the overall survival (OS) of patients in the TCGA-BRCA cohort. Functional enrichment analysis was conducted to investigate the biological meaning of these survival related genes. Then, patients in TCGA-BCRA were randomly divided into training set and test. Least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was performed and the risk score of BC patients in this model was used to build a prognostic signature. The prognostic performance of the signature was evaluated in the training set, test set, and an independent validation set GSE7390.2519 genes were demonstrated to be significantly associated with the OS of BC patients. Functional annotation of the 2519 genes suggested that these genes were associated with immune response and protein synthesis related gene ontology terms and pathways. 17 genes were identified in the LASSO Cox regression model and used to construct a 17-gene signature. Patients in the 17-gene signature low risk group have better OS and event-free survival compared with those in the 17-gene signature high risk group in the TCGA-BRCA cohort. The prognostic role of the 17-gene signature has been confirmed in the validation cohort. Multivariable Cox proportional hazards regression model suggested the 17-gene signature was an independent prognostic factor in BC.The 17-gene signature we developed could successfully classify patients into high- and low-risk groups, indicating that it might serve as candidate biomarker in BC.

Identification of prognostic splicing factors and exploration of their potential regulatory mechanisms in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD), the most common subtype of pancreatic cancer, is a highly lethal disease. In this study, we integrated the expression profiles of splicing factors (SFs) of PAAD from RNA-sequencing data to provide a comprehensive view of the clinical significance of SFs. A prognostic index (PI) based on SFs was developed using the least absolute shrinkage and selection operator (LASSO) COX analysis. The PI exhibited excellent performance in predicting the status of overall survival of PAAD patients. We also used the percent spliced in (PSI) value obtained from SpliceSeq software to quantify different types of alternative splicing (AS). The prognostic value of AS events was explored using univariate COX and LASSO COX analyses; AS-based PIs were also proposed. The integration of prognosis-associated SFs and AS events suggested the potential regulatory mechanisms of splicing processes in PAAD. This study defined the markedly clinical significance of SFs and provided novel insight into their potential regulatory mechanisms.

Identify potential clinical significance of long noncoding RNA forkhead box P4 antisense RNA 1 in patients with early stage pancreatic ductal adenocarcinoma

Previous studies have shown that forkhead box P4 antisense RNA 1 (FOXP4‐AS1) is dysregulated in tumor tissues and can serve as a prognostic indicator for multiple cancers. However, the clinical significance of FOXP4‐AS1 in pancreatic ductal adenocarcinoma (PDAC) remains unclear. The goal of this study is to recognize the possible clinical significance of long noncoding RNA FOXP4‐AS1 in patients with early stage PDAC. A total of 112 patients from The Cancer Genome Atlas (TCGA) PDAC cohort, receiving RNA sequencing, were involved in the study. Survival analysis, functional mechanism, and potential small molecule drugs of target therapy of FOXP4‐AS1 were performed in this study. Survival analysis in TCGA PDAC cohort suggested that patients with high FOXP4‐AS1 expression had significantly augmented possibility of death than in PDAC patients with lower FOXP4‐AS1 expression (adjusted P = .008; adjusted HR = 2.143, 95% CI = 1.221‐3.760). In this study, a genome‐wide RNA sequencing dataset was used to identify 927 genes co‐expressing with FOXP4‐AS1 in PDAC tumor tissues. A total of 676 differentially expressed genes were identified between different FOXP4‐AS1 expression groups. Functional enrichment analysis of these genes and gene set enrichment analysis for PDAC genome‐wide RNA sequencing dataset was done. We have found that FOXP4‐AS1 may function in PDAC by participating in biological processes and pathways including oxidative phosphorylation, tricarboxylic acid cycle, classical tumor‐related pathways such as NF‐kappaB as well as Janus kinase/signal transducers in addition to activators of transcription, cell proliferation, and adhesion. In addition, we also screened two potential targeted therapeutic small molecule drugs (dimenhydrinate and metanephrine) for FOXP4‐AS1 in PDAC. In conclusion, our present study demonstrated that higher expression of FOXP4‐AS1 in PDAC tumor tissues were related with an inferior medical outcome. Through multiple genome‐wide approaches, we identified the potential molecular mechanisms of FOXP4‐AS1 in PDAC and two targeted therapeutic drugs for it.

A Long Non-coding RNA Signature to Improve Prognostic Prediction of Pancreatic Ductal Adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive solid malignant tumors worldwide. Increasing investigations demonstrate that long non-coding RNAs (lncRNAs) expression is abnormally dysregulated in cancers. It is crucial to identify and predict the prognosis of patients for the selection of further therapeutic treatment.

Methods: PDAC lncRNAs abundance profiles were used to establish a signature that could better predict the prognosis of PDAC patients. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a multi-lncRNA signature in the TCGA training cohort (N = 107). The signature was then validated in a TCGA validation cohort (N = 70) and another independent Fudan cohort (N = 46).

Results: A five-lncRNA signature was constructed and it was significantly related to the overall survival (OS), either in the training or validation cohorts. Through the subgroup and Cox regression analyses, the signature was proven to be independent of other clinic-pathologic parameters. Receiver operating characteristic curve (ROC) analysis also indicated that our signature had a better predictive capacity of PDAC prognosis. Furthermore, ClueGO and CluePedia analyses showed that a number of cancer-related and drug response pathways were enriched in high risk groups.

Conclusions: Identifying the five-lncRNA signature (RP11-159F24.5, RP11-744N12.2, RP11-388M20.1, RP11-356C4.5, CTC-459F4.9) may provide insight into personalized prognosis prediction and new therapies for PDAC patients.

The Identification and Analysis of mRNA-lncRNA-miRNA Cliques From the Integrative Network of Ovarian Cancer

Ovarian cancer is one of the leading causes of cancer mortality in women. Since little clinical symptoms were shown in the early period of ovarian cancer, most patients were found in phases III-IV or with abdominal metastasis when diagnosed. The lack of effective early diagnosis biomarkers makes ovarian cancer difficult to screen. However, in essence, the fundamental problem is we know very little about the regulatory mechanisms during tumorigenesis of ovarian cancer. There are emerging regulatory factors, such as long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), which have played important roles in cancers. Therefore, we analyzed the RNA-seq profiles of 407 ovarian cancer patients. An integrative network of 20,424 coding RNAs (mRNAs), 10,412 lncRNAs, and 742 miRNAs were construed with variance inflation factor (VIF) regression method. The mRNA-lncRNA-miRNA cliques were identified from the network and analyzed. Such promising cliques showed significant correlations with survival and stage of ovarian cancer and characterized the complex sponge regulatory mechanism, suggesting their contributions to tumorigenicity. Our results provided novel insights of the regulatory mechanisms among mRNAs, lncRNAs, and miRNAs and highlighted several promising regulators for ovarian cancer detection and treatment.

An integrated mRNA-lncRNA signature for relapse prediction in laryngeal cancer

Patients with laryngeal cancer with early relapse usually have a poor prognosis. In this study, we aimed to identify a multi-gene signature to improve the relapse prediction in laryngeal cancer. One microarray data set GSE27020 (training set, N = 109) and one RNA-sequencing data set (validation set, N = 85) were included into the analysis. In the training set, the microarray expression profile was re-annotated into an mRNA-long noncoding RNA (lncRNA) biphasic profile. Then, LASSO Cox regression model identified nine relapse-related RNA (eight mRNA and one lncRNA), and a risk score was calculated for each sample according to the model coefficients. Patients with high-risk showed poorer relapse-free survival than patients with low risk (hazard ratios (HR): 6.189, 95% confidence interval (CI): 3.075-12.460, P < 0.0001). The risk score demonstrated good accuracy in predicting the relapse (area under time-dependent receiver-operating characteristic (AUC): 0.859 at 1 year, 0.822 at 3 years, and 0.815 at 5 years). The results were validated in the validation set (HR: 3.762, 95% CI: 1.594-8.877, P = 0.011; AUC: 0.770 at 1 year, 0.769 at 3 years, and 0.728 at 5 years). The multivariate analysis reached consistent results after adjustment by multiple confounders. When compared with a 27-gene signature, a 2-lncRNA signature, and Tumor-Node-Metastasis stage, the risk score also showed better performance (P < 0.05). In conclusion, we successfully developed a robust mRNA-lncRNA signature that can accurately predict the relapse in laryngeal cancer.

Integrated analysis reveals five potential ceRNA biomarkers in human lung adenocarcinoma

Background

Competing endogenous RNAs (ceRNAs) are a newly identified type of regulatory RNA. Accumulating evidence suggests that ceRNAs play an important role in the pathogenesis of diseases such as cancer. Thus, ceRNA dysregulation may represent an important molecular mechanism underlying cancer progression and poor prognosis. In this study, we aimed to identify ceRNAs that may serve as potential biomarkers for early diagnosis of lung adenocarcinoma (LUAD).

Methods

We performed differential gene expression analysis on TCGA-LUAD datasets to identify differentially expressed (DE) mRNAs, lncRNAs, and miRNAs at different tumor stages. Based on the ceRNA hypothesis and considering the synergistic or feedback regulation of ceRNAs, a lncRNA–miRNA–mRNA network was constructed. Functional analysis was performed using gene ontology term and KEGG pathway enrichment analysis and KOBAS 2.0 software. Transcription factor (TF) analysis was carried out to identify direct targets of the TFs associated with LUAD prognosis. Identified DE genes were validated using gene expression omnibus (GEO) datasets.

Results

Based on analysis of TCGA-LUAD datasets, we obtained 2,610 DE mRNAs, 915 lncRNAs, and 125 miRNAs that were common to different tumor stages (|log₂(Fold change)| ≥ 1, false discovery rate < 0.01), respectively. Functional analysis showed that the aberrantly expressed mRNAs were closely related to tumor development. Survival analyses of the constructed ceRNA network modules demonstrated that five of them exhibit prognostic significance. The five ceRNA interaction modules contained one lncRNA (FENDRR), three mRNAs (EPAS1, FOXF1, and EDNRB), and four miRNAs (hsa-miR-148a, hsa-miR-195, hsa-miR-196b, and hsa-miR-301b). The aberrant expression of one lncRNA and three mRNAs was verified in the LUAD GEO dataset. Transcription factor analysis demonstrated that EPAS1 directly targeted 13 DE mRNAs.

Conclusion

Our observations indicate that lncRNA-related ceRNAs and TFs play an important role in LUAD. The present study provides novel insights into the molecular mechanisms underlying LUAD pathogenesis. Furthermore, our study facilitates the identification of potential biomarkers for the early diagnosis and prognosis of LUAD and therapeutic targets for its treatment.