BACKGROUNDRecent evidence shows that long non-coding RNAs (lncRNAs) are closely related to hepatogenesis and a few aggressive features of hepatocellular carcinoma (HCC). Increasing studies demonstrate that lncRNAs are potential prognostic factors for HCC. Moreover, several studies reported the combination of lncRNAs for predicting the overall survival (OS) of HCC, but the results varied. Thus, more effort including more accurate statistical approaches is needed for exploring the prognostic value of lncRNAs in HCC.AIMTo develop a robust lncRNA signature associated with HCC recurrence to improve prognosis prediction of HCC.METHODSUnivariate COX regression analysis was performed to screen the lncRNAs significantly associated with recurrence-free survival (RFS) of HCC in GSE76427 for the least absolute shrinkage and selection operator (LASSO) modelling. The established lncRNA signature was validated and developed in The Cancer Genome Atlas (TCGA) series using Kaplan-Meier curves. The expression values of the identified lncRNAs were compared between the tumor and non-tumor tissues. Pathway enrichment of these lncRNAs was conducted based on the significantly co-expressed genes. A prognostic nomogram combining the lncRNA signature and clinical characteristics was constructed.RESULTSThe lncRNA signature consisted of six lncRNAs: MSC-AS1, POLR2J4, EIF3J-AS1, SERHL, RMST, and PVT1. This risk model was significantly associated with the RFS of HCC in the TCGA cohort with a hazard ratio (HR) being 1.807 (95%CI [confidence interval]: 1.329-2.457) and log-rank P-value being less than 0.001. The best candidates of the six-lncRNA signature were younger male patients with HBV infection in relatively early tumor-stage and better physical condition but with higher preoperative alpha-fetoprotein. All the lncRNAs were significantly upregulated in tumor samples compared to non-tumor samples (P < 0.05). The most significantly enriched pathways of the lncRNAs were TGF-β signaling pathway, cellular apoptosis-associated pathways, etc. The nomogram showed great utility of the lncRNA signature in HCC recurrence risk stratification.CONCLUSIONWe have constructed a six-lncRNA signature for prognosis prediction of HCC. This risk model provides new clinical evidence for the accurate diagnosis and targeted treatment of HCC.

Non-small cell lung cancer (NSCLC) is the most common cancer and cause of cancer-related mortality globally. Increasing evidence suggested that the long non-coding RNAs (lncRNAs) were involved in cancer-related death. To explore the possible prognostic lncRNA biomarkers for NSCLC patients, in the present study, we conducted a comprehensive lncRNA profiling analysis based on 1902 patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. In the discovery phase, we employed 682 patients from the combination of four GEO datasets (GSE30219, GSE31546, GSE33745 and GSE50081) and conducted a seven-lncRNA formula to predict overall survival (OS). Next, we validated our risk-score formula in two independent datasets, TCGA (n=994) and GSE31210 (n=226). Stratified analysis revealed that the seven-lncRNA signature was significantly associated with OS in stage I patients from both discovery and validation groups (all P<0.001). Additionally, the prognostic value of the seven-lncRNA signature was also found to be favorable in patients carrying wild-type KRAS or EGFR. Bioinformatical analysis suggested that the seven-lncRNA signature affected patients’ prognosis by influencing cell cycle-related pathways. In summary, our findings revealed a seven-lncRNA signature that predicted OS of NSCLC patients, especially in those with early tumor stage and carrying wild-type KRAS or EGFR.

Growing evidence indicates that long non-coding RNAs (lncRNAs) may be potential biomarkers and therapeutic targets for many disease conditions, including cancer. In this study, we constructed a risk score system of three lncRNAs ( LOC101927051 , LINC00667 and NSUN5P2 ) for predicting the prognosis of small hepatocellular carcinoma (sHCC) (maximum tumor diameter ≤5 cm). The prognostic value of this sHCC risk model was confirmed in TCGA HCC samples (TNM stage I and II). Stratified survival analysis revealed that the suitable patient groups of the sHCC lncRNA-signature included HBV-infected and cirrhotic patients with better physical conditions yet lower levels of albumin and higher levels of alpha-fetoprotein preoperatively. Besides, Asian patients with no family history of HCC or history of alcohol consumption can be predicted more precisely. Molecular functional analysis indicated that PYK2 pathway was significantly enriched in the high-risk patients. Pathway enrichment analysis indicated that the two lncRNAs ( LINC00667 and NSUN5P2 ) associated with poor prognosis were closely related to cell cycle. The nomogram based on the lncRNA-signature for RFS prediction in sHCC patients exhibited good performance in recurrence risk stratification. In conclusion, we identified a novel three-lncRNA-expression-based risk model for predicting the prognosis of sHCC.

Rationale: Recently, long non-coding RNAs (lncRNAs), known to be involved in human cancer progression, have been shown to encode peptides with biological functions, but the role of lncRNA-encoded peptides in cellular senescence is largely unexplored. We previously reported the tumor-suppressive role of PINT87aa, a peptide encoded by the long intergenic non-protein coding RNA, p53 induced transcript ( LINC-PINT). Here, we investigated PINT87aa's role in hepatocellular carcinoma (HCC) cellular senescence. Methods: We examined PINT87aa and truncated PINT87aa functions in vitro by monitoring cell proliferation and performed flow cytometry, senescence-associated β-galactosidase staining, JC-1 staining indicative of mitochondrial membrane potential, the ratio of the overlapping area of light chain 3 beta (LC3B) and mitochondrial probes and the ratio of lysosomal associated membrane protein 1 (LAMP1) overlapping with cytochrome c oxidase subunit 4I1 (COXIV) denoting mitophagy. PINT87aa and truncated PINT87aa functions in vivo were verified by subcutaneously transplanted tumors in nude mice. The possible binding between PINT87aa and forkhead box M1 (FOXM1) was predicted through structural analysis and verified by co-immunoprecipitation and immunofluorescence co-localization. Rescue experiments were performed in vivo and in vitro following FOXM1 overexpression. Further, chromatin immunoprecipitation, polymerase chain reaction, and dual-luciferase reporter gene assay were conducted to validate FOXM1 binding to the prohibitin 2 ( PHB2 ) promoter. Results: PINT87aa was significantly increased in the hydrogen peroxide-induced HCC cell senescence model. Overexpression of PINT87aa induced growth inhibition, cellular senescence, and decreased mitophagy in vitro and in vivo . In contrast, FOXM1 gain-of-function could partially reduce the proportion of senescent HCC cells and enhance mitophagy. PINT87aa overexpression did not affect the expression of FOXM1 itself but reduced that of its target genes involved in cell cycle and proliferation, especially PHB2, which was involved in mitophagy and transcribed by FOXM1. Structural analysis indicated that PINT87aa could bind to the DNA-binding domain of FOXM1, which was confirmed by co-immunoprecipitation and immunofluorescence co-localization. Furthermore, we demonstrated that the 2 to 39 amino acid truncated form of the peptide exerted effects similarly to the full form. Conclusion: Our study established the role of PINT87aa as a novel biomarker and a key regulator of cellular senescence in HCC and identified PINT87aa as a potential therapeutic target for HCC.

Pancreatic cancer (PC) is a highly malignant cancer with poor prognosis and high mortality. Aberrant DNA methylation plays a critical role in the occurrence, progression and prognosis of malignant tumors. In this study, we employed multiple datasets from APGI, TCGA and GEO to perform Multi-Omics analysis, including DNA methylation and expression profiling analysis. Three differentially expressed genes (SULT1E1, IGF2BP3, MAP4K4) with altered status of DNA methylation were identified and then enrolled into prognostic risk score model using LASSO regression. Univariate cox regression analysis indicated that high risk score was significantly associated with poor prognosis. Multivariate cox regression analysis proved the risk score was an independent prognostic factor for PC. In addition, time-dependent ROC curves indicated good performance of our model in predicting the 1-, 3- and 5-year survival of PC patients. Besides, stratified survival analysis revealed that the risk score model had greater prognostic value for patients of late stage with T3/T4 and N+. Pathway enrichment analysis suggested that these three genes might promote tumor progression by affecting signaling by Rho GTPases and chromosome segregation. In summary, three hypomethylated gene signature were significantly associated with patients’ overall survival, which might serve as potential prognostic biomarkers for PC patients.

BACKGROUND Cellular senescence is a recognized barrier for progression of chronic liver diseases to hepatocellular carcinoma (HCC). The expression of a cluster of genes is altered in response to environmental factors during senescence. However, it is questionable whether these genes could serve as biomarkers for HCC patients. AIM To develop a signature of senescence-associated genes (SAGs) that predicts patients’ overall survival (OS) to improve prognosis prediction of HCC. METHODS SAGs were identified using two senescent cell models. Univariate COX regression analysis was performed to screen the candidate genes significantly associated with OS of HCC in a discovery cohort (GSE14520) for the least absolute shrinkage and selection operator modelling. Prognostic value of this seven-gene signature was evaluated using two independent cohorts retrieved from the GEO (GSE14520) and the Cancer Genome Atlas datasets, respectively. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive accuracy of the seven-SAG signature and serum α-fetoprotein (AFP). RESULTS A total of 42 SAGs were screened and seven of them, including KIF18B , CEP55 , CIT , MCM7 , CDC45 , EZH2 , and MCM5 , were used to construct a prognostic formula. All seven genes were significantly downregulated in senescent cells and upregulated in HCC tissues. Survival analysis indicated that our seven-SAG signature was strongly associated with OS, especially in Asian populations, both in discovery and validation cohorts. Moreover, time-dependent ROC curve analysis suggested the seven-gene signature had a better predictive accuracy than serum AFP in predicting HCC patients’ 1-, 3-, and 5-year OS. CONCLUSION We developed a seven-SAG signature, which could predict OS of Asian HCC patients. This risk model provides new clinical evidence for the accurate diagnosis and targeted treatment of HCC.

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The elderly are the majority of patients with non-small cell lung cancer (NSCLC). Compared to the overall population's predictive guidance, an effective predictive guidance for elderly patients can better guide patients' postoperative treatment and improve overall survival (OS) and disease-free survival (DFS). Recently, the long non-coding RNAs (lncRNAs) have been found to play an important role in predicting tumor prognosis. To identify potential lncRNAs to predict survival in elderly patients with NSCLC, in the present study, we chose 456 elderly patients with NSCLC and analyzed differentially expressed lncRNAs from four Gene Expression Omnibus (GEO) datasets (GSE30219, GSE31546, GSE37745 and GSE50081). We then constructed an eight-lncRNA formula to predict elderly patients’ prognosis in NSCLC. Furthermore, we validated the prognostic values of the new risk model in two independent datasets, TCGA (n=670) and GSE31210 (n=130). Our data suggested a significant association between risk model and patients’ prognosis. Finally, stratification analysis further revealed the eight-lncRNA signature was an independent factor to predict OS and DFS in stage I elderly patients from both the discovery and validation groups. Functional prediction revealed that 8 lncRNAs have potential effects on tumor immune processes such as lymphocyte activation and TNF production in NSCLC. In summary, our data provides evidence that the eight-lncRNA signature could serve as an independent biomarker to predict prognosis in elderly patients with NSCLC especially in elderly stage I patients.