Background: Epigenetic alterations are involved in various aspects of colorectal carcinogenesis. N 6methyladenosine (m 6 A) modifications of RNAs are emerging as a new layer of epigenetic regulation. As the most abundant chemical modification of eukaryotic mRNA, m 6 A is essential for the regulation of mRNA stability, splicing, and translation. Alterations of m 6 A regulatory genes play important roles in the pathogenesis of a variety of human diseases. However, whether this mRNA modification participates in the glucose metabolism of colorectal cancer (CRC) remains uncharacterized. Methods: Transcriptome-sequencing and liquid chromatography-tandem mass spectrometry (LC-MS) were performed to evaluate the correlation between m 6 A modifications and glucose metabolism in CRC. Mass spectrometric metabolomics analysis, in vitro and in vivo experiments were conducted to investigate the effects of METTL3 on CRC glycolysis and tumorigenesis. RNA MeRIP-sequencing, immunoprecipitation and RNA stability assay were used to explore the molecular mechanism of METTL3 in CRC. Results: A strong correlation between METTL3 and 18 F-FDG uptake was observed in CRC patients from Xuzhou Central Hospital. METTL3 induced-CRC tumorigenesis depends on cell glycolysis in multiple CRC models. Mechanistically, METTL3 directly interacted with the 5′/3'UTR regions of HK2, and the 3'UTR region of SLC2A1 (GLUT1), then further stabilized these two genes and activated the glycolysis pathway. M 6 A-mediated HK2 and SLC2A1 (GLUT1) stabilization relied on the m 6 A reader IGF2BP2 or IGF2BP2/3, respectively. Conclusions: METTL3 is a functional and clinical oncogene in CRC. METTL3 stabilizes HK2 and SLC2A1 (GLUT1) expression in CRC through an m 6 A-IGF2BP2/3-dependent mechanism. Targeting METTL3 and its pathway offer alternative rational therapeutic targets in CRC patients with high glucose metabolism.

Long non-coding RNAs (lncRNAs) contribute to colorectal cancer (CRC). However, the role of lncRNAs in CRC metabolism, especially glucose metabolism remains largely unknown. In this study, we identify a lncRNA, GLCC1, which is significantly upregulated under glucose starvation in CRC cells, supporting cell survival and proliferation by enhancing glycolysis. Mechanistically, GLCC1 stabilizes c-Myc transcriptional factor from ubiquitination by direct interaction with HSP90 chaperon and further specifies the transcriptional modification pattern on c-Myc target genes, such as LDHA , consequently reprogram glycolytic metabolism for CRC proliferation. Clinically, GLCC1 is associated with tumorigenesis, tumor size and predicts poor prognosis. Thus, GLCC1 is mechanistically, functionally, and clinically oncogenic in colorectal cancer. Targeting GLCC1 and its pathway may be meaningful for treating patients with colorectal cancer.

BackgroundIncreasing evidence suggests long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers, however, few related lncRNA signatures have been established for prediction of cancer prognosis. We aimed at developing alncRNA signature to improve prognosis prediction of gastric cancer (GC).MethodsUsing a lncRNA-mining approach, we performed lncRNA expression profiling in large GC cohorts from Gene Expression Ominus (GEO), including GSE62254 data set (N = 300) and GSE15459 data set (N = 192). We established a set of 24-lncRNAs that were significantly associated with the disease free survival (DFS) in the test series.ResultsBased on this 24-lncRNA signature, the test series patients could be classified into high-risk or low-risk subgroup with significantly different DFS (HR = 1.19, 95 % CI = 1.13–1.25, P < 0.0001). The prognostic value of this 24-lncRNA signature was confirmed in the internal validation series and another external validation series, respectively. Further analysis revealed that the prognostic value of this signature was independent of lymph node ratio (LNR) and postoperative chemotherapy. Gene set enrichment analysis (GSEA) indicated that high risk score group was associated with several cancer recurrence and metastasis associated pathways.ConclusionsThe identification of the prognostic lncRNAs indicates the potential roles of lncRNAs in GC biogenesis. Our results may provide an efficient classification tool for clinical prognosis evaluation of GC.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-016-0544-0) contains supplementary material, which is available to authorized users.

Pancreatic cancer is an increasingly common cause of cancer mortality with a tight correspondence between disease mortality and incidence. Furthermore, it is usually diagnosed at an advanced stage with a very dismal prognosis. Due to the high heterogeneity, metabolic reprogramming, and dense stromal environment associated with pancreatic cancer, patients benefit little from current conventional therapy. Recent insight into the biology and genetics of pancreatic cancer has supported its molecular classification, thus expanding clinical therapeutic options. In this review, we summarize how the biological features of pancreatic cancer and its metabolic reprogramming as well as the tumor microenvironment regulate its development and progression. We further discuss potential biomarkers for pancreatic cancer diagnosis, prediction, and surveillance based on novel liquid biopsies. We also outline recent advances in defining pancreatic cancer subtypes and subtype-specific therapeutic responses and current preclinical therapeutic models. Finally, we discuss prospects and challenges in the clinical development of pancreatic cancer therapeutics.

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The E3 ubiquitin ligase RNF6 (RING-finger protein 6) plays a crucial role in carcinogenesis. However, the copy number and expression of RNF6 were rarely reported in colorectal cancer. We aimed to explore the mechanical, biological, and clinical role of RNF6 in colorectal cancer initiation and progression. The copy number and expression of RNF6 were analyzed from Tumorscape and The Cancer Genome Atlas (TCGA) datasets. Gene expressions were examined by real-time PCR, Western blot, and immunohistochemical staining. Gene expression profiling studies were performed to identify pivotal genes regulated by RNF6. Biological function of RNF6 on tumor growth and metastasis was detected and Role of RNF6 in modulating SHP-1 expression was examined by coimmunoprecipitation and confocal microscopy, respectively. The copy number of RNF6 was significantly amplified in colorectal cancer, and the amplification was associated with RNF6 expression level. Amplification and overexpression of RNF6 positively correlated with patients with colorectal cancer with poor prognosis. The gene set enrichment analysis (GSEA) revealed cell proliferation, and invasion-related genes were enriched in RNF6 high-expressed colorectal cancer cells as well as in patients from TCGA dataset. Downregulation of RNF6 impaired the colorectal cancer cell proliferation and invasion and RNF6 may activate the JAK/STAT3 pathway and increase pSTAT3 levels by inducing the ubiquitination and degradation of SHP-1. Genomic amplification drives RNF6 overexpression in colorectal cancer. RNF6 may be a novel biomarker in colorectal carcinogenesis, and RNF6 may increase pSTAT3 level via promoting SHP-1 ubiquitylation and degradation. Targeting the RNF6/SHP-1/STAT3 axis provides a potential therapeutic option for RNF6-amplified tumors. .

High throughput gene expression profiling has showed great promise in providing insight into molecular mechanisms. Metastasis‐related mRNAs may potentially enrich genes with the ability to predict cancer recurrence, therefore we attempted to build a recurrence‐associated gene signature to improve prognostic prediction of colorectal cancer (CRC). We identified 2848 differentially expressed mRNAs by analyzing CRC tissues with or without metastasis. For the selection of prognostic genes, a LASSO Cox regression model (least absolute shrinkage and selection operator method) was employed. Using this method, a 13‐mRNA signature was identified and then validated in two independent Gene Expression Omnibus cohorts. This classifier could successfully discriminate the high‐risk patients in discovery cohort [hazard ratio (HR) = 5.27, 95% confidence interval (CI) 2.30–12.08, P < 0.0001). Analysis in two independent cohorts yielded consistent results (GSE14333: HR = 4.55, 95% CI 2.18–9.508, P < 0.0001; GSE33113: HR = 3.26, 95% CI 2.16–9.16, P = 0.0176). Further analysis revealed that the prognostic value of this signature was independent of tumor stage, postoperative chemotherapy and somatic mutation. Receiver operating characteristic (ROC) analysis showed that the area under ROC curve of this signature was 0.8861 and 0.8157 in the discovery and validation cohort, respectively. A nomogram was constructed for clinicians, and did well in the calibration plots. Furthermore, this 13‐mRNA signature outperformed other known gene signatures, including oncotypeDX colon cancer assay. Single‐sample gene‐set enrichment analysis revealed that a group of pathways related to drug resistance, cancer metastasis and stemness were significantly enriched in the high‐risk patients. In conclusion, this 13‐mRNA signature may be a useful tool for prognostic evaluation and will facilitate personalized management of CRC patients.

Current studies indicate that long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers and implicated with prognosis in gastric cancer (GC). We intended to generate a multi-lncRNA signature to improve prognostic prediction of GC. By analyzing ten paired GC and adjacent normal mucosa tissues, 339 differentially expressed lncRNAs were identified as the candidate prognostic biomarkers in GC. Then we used LASSO Cox regression method to build a 12-lncRNA signature and validated it in another independent GEO dataset. An innovative 12-lncRNA signature was established, and it was significantly associated with the disease free survival (DFS) in the training dataset. By applying the 12-lncRNA signature, the training cohort patients could be categorized into high-risk or low-risk subgroup with significantly different DFS (HR = 4.52, 95%CI= 2.49-8.20, P < 0.0001). Similar results were obtained in another independent GEO dataset (HR=1.58, 95%CI=1.05 - 2.38, P=0.0270). Further analysis showed that the prognostic value of this 12-lncRNA signature was independent of AJCC stage and postoperative chemotherapy. Receiver operating characteristic (ROC) analysis showed that the area under receiver operating characteristic curve (AUC) of combined model reached 0.869. Additionally, a well-performed nomogram was constructed for clinicians. Moreover, single-sample gene-set enrichment analysis (ssGSEA) showed that a group of pathways related to drug resistance and cancer metastasis significantly enriched in the high risk patients. A useful innovative 12-lncRNA signature was established for prognostic evaluation of GC. It might complement clinicopathological features and facilitate personalized management of GC.