Prognostic Fifteen-Gene Signature for Early Stage Pancreatic Ductal Adenocarcinoma

The outcomes of patients treated with surgery for early stage pancreatic ductal adenocarcinoma (PDAC) are variable with median survival ranging from 6 months to more than 5 years. This challenge underscores an unmet need for developing personalized medicine strategies to refine the current treatment decision-making process. To derive a prognostic gene signature for patients with early stage PDAC, a PDAC cohort from Moffitt Cancer Center (n = 63) was used with overall survival (OS) as the primary endpoint. This was further evaluated using an independent microarray cohort dataset (Stratford et al: n = 102). Technical validation was performed by NanoString platform. A prognostic 15-gene signature was developed and showed a statistically significant association with OS in the Moffitt cohort (hazard ratio [HR] = 3.26; p<0.001) and Stratford et al cohort (HR = 2.07; p = 0.02), and was independent of other prognostic variables. Moreover, integration of the signature with the TNM staging system improved risk prediction (p<0.01 in both cohorts). In addition, NanoString validation showed that the signature was robust with a high degree of reproducibility and the association with OS remained significant in the two cohorts. The gene signature could be a potential prognostic tool to allow risk-adapted stratification of PDAC patients into personalized treatment protocols; possibly improving the currently poor clinical outcomes of these patients.     

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.     

Methylation profiling identified novel differentially methylated markers including OPCML and FLRT2 in prostate cancer

To develop new methods to distinguish indolent from aggressive prostate cancers (PCa), we utilized comprehensive high-throughput array-based relative methylation (CHARM) assay to identify differentially methylated regions (DMRs) throughout the genome, including both CpG island (CGI) and non-CGI regions in PCa patients based on Gleason grade. Initially, 26 samples, including 8 each of low [Gleason score (GS) 6] and high (GS ≥7) grade PCa samples and 10 matched normal prostate tissues, were analyzed as a discovery cohort. We identified 3,567 DMRs between normal and cancer tissues, and 913 DMRs distinguishing low from high-grade cancers. Most of these DMRs were located at CGI shores. The top 5 candidate DMRs from the low vs. high Gleason comparison, including OPCML, ELAVL2, EXT1, IRX5, and FLRT2, were validated by pyrosequencing using the discovery cohort. OPCML and FLRT2 were further validated in an independent cohort consisting of 20 low-Gleason and 33 high-Gleason tissues. We then compared patients with biochemical recurrence (n=70) vs. those without (n=86) in a third cohort, and they showed no difference in methylation at these DMR loci. When GS 3+4 cases and GS 4+3 cases were compared, OPCML-DMR methylation showed a trend of lower methylation in the recurrence group (n=30) than in the no-recurrence (n=52) group. We conclude that whole-genome methylation profiling with CHARM revealed distinct patterns of differential DNA methylation between normal prostate and PCa tissues, as well as between different risk groups of PCa as defined by Gleason scores. A panel of selected DMRs may serve as novel surrogate biomarkers for Gleason score in PCa.     

Autophagy-related circRNA evaluation reveals hsa_circ_0001747 as a potential favorable prognostic factor for biochemical recurrence in patients with prostate cancer

Prostate cancer (PCa) is a common high-incidence malignancy in men, some of whom develop biochemical recurrence (BCR) in the advanced stage. However, there are currently no accurate prognostic indicators of BCR in PCa. The aim of our study was to identify an autophagy-related circular RNA prognostic factor of BCR for patients with PCa. In this study, immunochemistry revealed that the classic autophagy marker MAP1LC3B was positively correlated with Gleason score. Least absolute shrinkage and selector operator regression were conducted to develop a novel prognostic model with tenfold cross-validation and an L1 penalty. Five autophagy-related circRNA signatures were included in the prognostic model. Patients with PCa were ultimately divided into high- and low-risk groups, based on the median risk score. Patients with PCa, who had a high risk score, were more likely to develop BCR in a shorter period of time. Univariate and multivariate Cox regression analyses demonstrated that the risk score was an independent variable for predicting BCR in PCa. In addition, a prognostic nomogram integrated with the risk score and numerous clinicopathological parameters was developed to accurately predict 3- and 5-year BCR of patients with PCa. Finally, the hsa_circ_0001747 signature was selected for further experimental verification in vitro and in vivo, which showed that downregulated hsa_circ_0001747 might facilitate PCa via augmenting autophagy. Our findings indicate that the autophagy-related circRNA signature hsa_circ_0001747 may serve as a promising indicator for BCR prediction in patients with PCa.Subject terms: Tumour biomarkers, Macroautophagy     

Identification of m6A methyltransferase-related lncRNA signature for predicting immunotherapy and prognosis in patients with hepatocellular carcinoma

N6-methyladenosine (m6A) methyltransferase has been shown to be an oncogene in a variety of cancers. Nevertheless, the relationship between the long non-coding RNAs (lncRNAs) and hepatocellular carcinoma (HCC) remains elusive. We integrated the gene expression data of 371 HCC and 50 normal tissues from The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression and Kaplan–Meier (K–M) analysis were performed to identify m6A methyltransferase-related lncRNAs. Three prognostic lncRNAs were selected by univariate and LASSO Cox regression analyses to construct the m6A methyltransferase-related lncRNA signature. Multivariate Cox regression analyses illustrated that this signature was an independent prognostic factor for overall survival (OS) prediction. The Gene Set Enrichment Analysis (GSEA) suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes (BPs) and pathways. Besides, we discovered that the lncRNAs signature was correlated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. Tumor Immune Dysfunction and Exclusion (TIDE) analysis revealed that the lncRNAs could predict the clinical response to immunotherapy. Our study had originated a prognostic signature for HCC based on the potential prognostic m6A methyltransferase-related lncRNAs. The present study had deepened the understanding of the TME status of HCC patients and laid a theoretical foundation for the choice of immunotherapy.     

Analysis of substrate specificity of the PaeR7 endonuclease: effect of base methylation on the kinetics of cleavage.

In murine cells expressing the PaeR7 endonuclease and methylase genes, the recognition sites (CTCGAG) of these enzymes can be methylated at the adenine residue by the PaeR7 methylase and at the internal cytosine by the mouse DNA methyltransferase. Using nonadecameric duplex deoxyoligonucleotide substrates, the specificity of the PaeR7 endonuclease for unmethylated, hemi-methylated, and fully methylated N6-methyladenine (m6A) and C5-methylcytosine (m5C) versions of these substrates has been studied. The Km, Kcat, and Ki values for these model substrates have been measured and suggest that fully or hemi-m6A-methylated PaeR7 sites in the murine genome are completely protected. However, the reactivity of fully or hemi-m5C-methylated PaeR7 sites is depressed 2900- and 100-fold respectively, compared to unmodified PaeR7 sites. The implications of the kinetic constants of the PaeR7 endonuclease for these methylated recognition sites as they occur in murine cells expressing this endonuclease gene are discussed.     

HJURP promotes proliferation in prostate cancer cells through increasing CDKN1A degradation via the GSK3β/JNK signaling pathway

Genes with cross-cancer aberrations are most likely to be functional genes or potential therapeutic targets. Here, we found a total of 137 genes were ectopically expressed in eight cancer types, of which Holliday junction recognition protein (HJURP) was significantly upregulated in prostate cancer (PCa). Moreover, patients with higher HJURP mRNA and protein levels had poorer outcomes, and the protein levels served as an independent prognosis factor for the overall survival of PCa patients. Functionally, ectopic HJURP expression promoted PCa cells proliferation in vitro and in vivo. Mechanistically, HJURP increased the ubiquitination of cyclin-dependent kinase inhibitor 1 (CDKN1A) via the GSK3β/JNK signaling pathway and decreased its stability. This study investigated the role of HJURP in PCa proliferation and may provide a novel prognostic and therapeutic target for PCa.Subject terms: Tumour biomarkers, Prostate cancer, Cell growth, Diseases, Molecular biology     

A metabolism-relevant signature as a predictor for prognosis and therapeutic response in pancreatic cancer

Although several altered metabolic genes have been identified to be involved in the tumorigenesis and advance of pancreatic cancer (PC), their prognostic values remained unclear. The purpose of this study was to explore new targets and establish a metabolic signature to predict prognosis and chemotherapy response for optimal individualized treatment. The expression data of PC patients from two independent cohorts and metabolism-related genes from KEGG were utilized and analyzed for the establishment of the signature via lasso regression. Then, the differentially expressed candidate genes were further confirmed via online data mining platform and qRT-PCR of clinical specimens. Then, the analyses of gene set enrichment, mutation, and chemotherapeutic response were performed via R package. As results showed, 109 differentially expressed metabolic genes were screened out in PC. Then a metabolism-related five-gene signature comprising B3GNT3, BCAT1, KYNU, LDHA, and TYMS was constructed and showed excellent ability for predicting survival. A novel nomogram coordinating the metabolic signature and other independent prognostic parameters was developed and showed better predictive power in predicting survival. In addition, this metabolic signature was significantly involved in the activation of multiple oncological pathways and regulation of the tumor immune microenvironment. The patients with high risk scores had higher tumor mutation burdens and were prone to be more sensitive to chemotherapy. In summary, our work identified a new metabolic signature and established a superior prognostic nomogram which may supply more indications to explore novel strategies for diagnosis and treatment.     

Regulation of AR mRNA translation in response to acute AR pathway inhibition

We report a new mechanism of androgen receptor (AR) mRNA regulation and cytoprotection in response to AR pathway inhibition (ARPI) stress in prostate cancer (PCA). AR mRNA translation is coordinately regulated by RNA binding proteins, YTHDF3 and G3BP1. Under ambient conditions m6A-modified AR mRNA is bound by YTHDF3 and translationally stimulated, while m6A-unmodified AR mRNA is bound by G3BP1 and translationally repressed. When AR-regulated PCA cell lines are subjected to ARPI stress, m6A-modified AR mRNA is recruited from actively translating polysomes (PSs) to RNA-protein stress granules (SGs), leading to reduced AR mRNA translation. After ARPI stress, m6A-modified AR mRNA liquid–liquid phase separated with YTHDF3, while m6A-unmodified AR mRNA phase separated with G3BP1. Accordingly, these AR mRNA messages form two distinct YTHDF3-enriched or G3BP1-enriched clusters in SGs. ARPI-induced SG formation is cell-protective, which when blocked by YTHDF3 or G3BP1 silencing increases PCA cell death in response to ARPI stress. Interestingly, AR mRNA silencing also delays ARPI stress-induced SG formation, highlighting its supportive role in triggering this stress response. Our results define a new mechanism for stress adaptive cell survival after ARPI stress involving SG-regulated translation of AR mRNA, mediated by m6A RNA modification and their respective regulatory proteins.     

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (−0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.     

Prognostic and predictive role of a metabolic rate‐limiting enzyme signature in hepatocellular carcinoma

ObjectivesAbnormal expression of metabolic rate‐limiting enzymes drives the occurrence and progression of hepatocellular carcinoma (HCC). This study aimed to elucidate the comprehensive model of metabolic rate‐limiting enzymes associated with the prognosis of HCC.Materials and MethodsHCC animal model and TCGA project were used to screen out differentially expressed metabolic rate‐limiting enzyme. Cox regression, least absolute shrinkage and selection operation (LASSO) and experimentally verification were performed to identify metabolic rate‐limiting enzyme signature. The area under the receiver operating characteristic curve (AUC) and prognostic nomogram were used to assess the efficacy of the signature in the three HCC cohorts (TCGA training cohort, internal cohort and an independent validation cohort).ResultsA classifier based on three rate‐limiting enzymes (RRM1, UCK2 and G6PD) was conducted and serves as independent prognostic factor. This effect was further confirmed in an independent cohort, which indicated that the AUC at year 5 was 0.715 (95% CI: 0.653‐0.777) for clinical risk score, whereas it was significantly increased to 0.852 (95% CI: 0.798‐0.906) when combination of the clinical with signature risk score. Moreover, a comprehensive nomogram including the signature and clinicopathological aspects resulted in significantly predict the individual outcomes.ConclusionsOur results highlighted the prognostic value of rate‐limiting enzymes in HCC, which may be useful for accurate risk assessment in guiding clinical management and treatment decisions.     

Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso-Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.     

Transcriptional landscape of DNA repair genes underpins a pan-cancer prognostic signature associated with cell cycle dysregulation and tumor hypoxia

Overactive DNA repair contributes to therapeutic resistance in cancer. However, pan-cancer comparative studies investigating the contribution of all DNA repair genes in cancer progression employing an integrated approach have remained limited. We performed a multi-cohort retrospective analysis to determine the prognostic significance of 138 DNA repair genes in 16 cancer types (n = 16,225). Cox proportional hazards analyses revealed a significant variation in the number of prognostic genes between cancers; 81 genes were prognostic in clear cell renal cell carcinoma while only two genes were prognostic in glioblastoma. We reasoned that genes that were commonly prognostic in highly correlated cancers revealed by Spearman’s correlation analysis could be harnessed as a molecular signature for risk assessment. A 10-gene signature, uniting prognostic genes that were common in highly correlated cancers, was significantly associated with overall survival in patients with clear cell renal cell (P < 0.0001), papillary renal cell (P = 0.0007), liver (P = 0.002), lung (P = 0.028), pancreas (P = 0.00013) or endometrial (P = 0.00063) cancers. Receiver operating characteristic analyses revealed that a combined model of the 10-gene signature and tumor staging outperformed either classifier when considered alone. Multivariate Cox regression models incorporating additional clinicopathological features showed that the signature was an independent predictor of overall survival. Tumor hypoxia is associated with adverse outcomes. Consistent across all six cancers, patients with high 10-gene and high hypoxia scores had significantly higher mortality rates compared to those with low 10-gene and low hypoxia scores. Functional enrichment analyses revealed that high mortality rates in patients with high 10-gene scores were attributable to an overproliferation phenotype. Death risk in these patients was further exacerbated by concurrent mutations of a cell cycle checkpoint protein, TP53. The 10-gene signature identified tumors with heightened DNA repair ability. This information has the potential to radically change prognosis through the use of adjuvant DNA repair inhibitors with chemotherapeutic drugs.