Epigenetic alterations are strongly associated with the development of cancer. The aim of this study was to identify epigenetic pattern in squamous cell lung cancer (LUSC) on a genome-wide scale. Here we performed DNA methylation profiling on 24 LUSC and paired non-tumor lung (NTL) tissues by Illumina Human Methylation 450 K BeadArrays, and identified 5214 differentially methylated probes. By integrating DNA methylation and mRNA expression data, 449 aberrantly methylated genes accompanied with altered expression were identified. Ingenuity Pathway analysis highlighted these genes which were closely related to the carcinogenesis of LUSC, such as ERK family, NFKB signaling pathway, Hedgehog signaling pathway, providing new clues for understanding the molecular mechanisms of LUSC pathogenesis. To verify the results of high-throughput screening, we used 56 paired independent tissues for clinical validation by pyrosequencing. Subsequently, another 343 tumor tissues from the Cancer Genome Atlas (TCGA) database were utilized for further validation. Then, we identified a panel of DNA methylation biomarkers (CLDN1, TP63, TBX5, TCF21, ADHFE1 and HNF1B) in LUSC. Furthermore, we performed receiver operating characteristics (ROC) analysis to assess the performance of biomarkers individually, suggesting that they could be suitable as potential diagnostic biomarkers for LUSC. Moreover, hierarchical clustering analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation. Collectively, these results suggest that DNA methylation plays critical roles in lung tumorigenesis and may potentially be proposed as a diagnostic biomarker. ChiCTR-RCC-12002830 Date of registration: 2012–12-17.

Cancer stem cells (CSCs) are a major cause of tumor treatment resistance, relapse and metastasis. Cancer cells exhibit reprogrammed metabolism characterized by aerobic glycolysis, which is also critical for sustaining cancer stemness. However, regulation of cancer cell metabolism rewiring and stemness is not completely understood. Here, we report that ETV4 is a key transcription factor in regulating glycolytic gene expression. ETV4 loss significantly inhibits the expression of HK2, LDHA as well as other glycolytic enzymes, reduces glucose uptake and lactate release in breast cancer cells. In human breast cancer and hepatocellular carcinoma tissues, ETV4 expression is positively correlated with glycolytic signaling. Moreover, we confirm that breast CSCs (BCSCs) are glycolysis-dependent and show that ETV4 is required for BCSC maintenance. ETV4 is enriched in BCSCs, its knockdown and overexpression suppresses and promotes breast cancer cell stem-like traits, respectively. Mechanistically, on the one hand, we find that ETV4 may enhance glycolysis activity to facilitate breast cancer stemness; on the other, ETV4 activates Sonic Hedgehog signaling by transcriptionally promoting CXCR4 expression. A xenograft assay validates the tumor growth-impeding effect and inhibition of CXCR4/SHH/GLI1 signaling cascade after ETV4 depletion. Together, our study highlights the potential roles of ETV4 in promoting cancer cell glycolytic shift and BCSC maintenance and reveals the molecular basis.

Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR.

The vitamin K antagonist is a commonly prescribed effective oral anticoagulant with a narrow therapeutic range, and the dose requirements for different patients varied greatly. In recent years, studies on human intestinal microbiome have provided many valuable insights into disease development and drug reactions. A lot of studies indicated the potential relationship between microbiome and the vitamin K antagonist. Vitamin K is absorbed by the gut, and the intestinal bacteria are a major source of vitamin K in human body. A combined use of the vitamin K antagonist and antibiotics may result in an increase in INR, thus elevating the risk of bleeding, while vitamin K supplementation can improve stability of anticoagulation for oral vitamin K antagonist treatment. Recently, how intestinal bacteria affect the response of the vitamin K antagonist remains unclear. In this review, we reviewed the research, focusing on the physiology of vitamin K in the anticoagulation treatment, and investigated the potential pathways of intestinal bacteria affecting the reaction of the vitamin K antagonist.

Background: DCBLD2 is highly expressed in various cancers, including colorectal cancer. DCBLD2 overexpression promotes tumor occurrence, development, and metastasis. However, DCBLD2 sensitivity to chemotherapy drugs and its mechanism on tumor development are unknown.Methods: DCBLD2 expression differences in cancer and normal tissues were obtained from GEO and TCGA databases. DCBLD2 influence on prognosis was also compared, and the database analysis results were verified via the analysis of clinical samples. GDSC database was used to analyze the effect of DCBLD2 expression difference on 5-FU drug sensitivity on tumor cells. CCK-8, clone formation, scratch, Transwell invasion and migration assays were used to assess DCBLD2 effects on the proliferation, metastasis, and 5-FU drug sensitivity on HCT116 and Caco-2 colorectal cancer cells. Angiogenesis and Matrigel plug assays were used to study the effect of DCBLD2 on angiogenesis. Q-RCR and Western Blot were used to analyze DCBLD2 impact on the EMT signaling pathway, and TAP-MS assay with Co-IP verification was used to identify the downstream target proteins binding to DCBLD2.Results: Both database and clinical sample validation results showed that the expression of DCBLD2 in colorectal cancer tissues was significantly higher than that in normal tissues, leading to poor prognosis of patients. GDSC database analysis showed that DCBLD2 overexpression caused tumor cell resistance to 5-FU. The results of in vitro and in vivo experiments showed that the inhibition of DCBLD2 reduced the proliferation, migration and invasion of colorectal cancer cells, inhibited the angiogenesis of endothelial cells, and enhanced the drug sensitivity to 5-FU. The results of q-RCR and Western Blot experiments showed that the inhibition of DCBLD2 can suppress the EMT signal. The results of TAP-MS assay showed that the proteins bound to DCBLD2 were enriched to the Focal adhesion pathway. The results of Co-IP assay show that DCBLD2 can combine with ITGB1, the key factor of Focal adhesion pathway.Conclusion: DCBLD2 may affect the development of colorectal cancer by regulating cell proliferation and motility, and modulate 5-FU resistance. Down-regulation of DCBLD2 can inhibit EMT signal and angiogenesis. DCBLD2 can combine with ITGB1, the key signal factor of the Focal adhesion pathway.