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The death receptor 5 (DR5) is a member of the tumor necrosis factor receptor superfamily that can transduce the apoptosis signal in cells. This study assessed serum levels of soluble death receptor 5 (sDR5) in small-cell lung cancer (SCLC) patients compared with those in healthy controls. Clinicopathological features of patients, treatment responses, and overall survival of patients were also recorded and analyzed. The sDR5 levels were analyzed using ELISA in 50 healthy controls and 82 SCLC patients before and after first-line chemotherapy. The statistical data showed that pre-treatment levels of serum sDR5 in SCLC patients were higher than those of healthy controls (P

Background Osteopontin (OPN) is closely related to tumor occurrence and metastasis. This study explored the clinical value of serum OPN levels in small cell lung cancer (SCLC) patients. Methods The ELISA method was used to determine the OPN level of 96 SCLC patients before and after first-line chemotherapy, and compared with 60 healthy controls. Results The serum OPN level of SCLC patients before treatment was significantly higher than that of the healthy control (P P = 0.012, 0.034, and 0.037, respectively). Serum OPN level decreased after first-line chemotherapy (P = 0.019), which was related to treatment response (P = 0.011). The serum OPN level was an independent predictor of overall survival. Conclusions The serum OPN level can be used as a biomarker to predict treatment response and survival of SCLC patients.

Risperidone has been used to treat the symptoms of schizophrenia and to reduce its relapse. However, the responses to treatment show great variability among patients. The potassium channel has been reported as an effective target for antipsychotics. KCNH7, a member of the voltage-gated K+ channel Kv11 family, is primarily expressed in the brain. Here, we assessed the genetic association of KCNH7 with risperidone responses in 393 schizophrenia patients. The patients were treated with risperidone for 6 weeks. The reduction rates of Positive and Negative Syndrome Scale (PANSS) scores were determined to quantify drug response. We also examined the associations between six single-nucleotide polymorphisms (SNPs) of KCNH7 and the risperidone responses for a total of 6 weeks. The SNP rs77699177 (C > T) in the KCNH7 gene intron was significantly associated with the treatment response reflected by the PANSS reduction rate (CC, 55.8 ± 23.0; TC, 70.9 ± 20.3, P = 0.000110), indicating that patients with the TC genotype have better efficacy for antipsychotic therapy. The rs2241240 SNP also showed a significant association with treatment responses after 6 weeks of treatment (P = 0.00256). The findings indicate that the voltage-gated K+ channel KCNH7 is a potential functional marker for the identification of the response to risperidone treatment in schizophrenia patients.Note: The study was registered under clinical trial number ChiCTR-RNC-09000522 (http://www.chictr.org/).

Background Glutathione S‐transferases (GSTs) polymorphisms may impact on chronic myeloid leukemia (CML) risk or heterogeneous responses to Imatinib mesylate (IM). The aim of this study was to evaluate the correlation between GSTs polymorphisms and CML risk, treatment response. Methods We genotyped GSTM1, GSTT1 null deletion polymorphisms, and GSTP1 Ile105Val polymorphism by PCR methods and BCR‐ABL transcripts were analyzed by qRT‐PCR in 104 CML patients and 104 sex‐ and age‐matched healthy individuals. Results Individual analysis showed significant association of GSTM1 (p = 0.008; OR = 0.46; 95% CI: 0.26–0.82) and GSTP1 genes (p = 0.04; OR = 1.56; 95% CI: 1.016–2.423) with CML risk. The combined analysis indicated that GSTM1 null/GSTT1 present, GSTM1‐null/GSTP1M*(AG/GG) as well as GSTT1 present/ GSTP1M* genotype were associated with CML risk (ORg(‐):2.28; 95% CI: 1.29–4.04; ORgg: 2.85; 95% CI: 1.36–5.97; OR(‐)g: 1.75; 95% CI: 0.99–3.06, respectively). The proportion of CML cancer attributable to the interaction of smoking and GSTM1 null, GSTT1null, and GSTP1 M* was 42%, 39%, and 13%, respectively. Patients with GSTM1‐null and GSTP1 AG/GG genotype had significantly a lower rate of MMR achievement (p = 0.00; p = 0.009 respectively). Event‐free survival (EFS) percentage was similar between GSTM1 null and GSTM1 present patients (p = 0.21). Conclusion Our study suggests the influence of GSTM1 and GSTP1 polymorphisms on CML risk and treatment response. The interaction between GSTs polymorphisms and smoking plays a significant role on CML susceptibility.

In human cells, three closely related RAS genes, termed HRAS, KRAS, and NRAS, encode four highly homologous proteins. RAS proteins are small GTPases involved in a broad spectrum of key molecular and cellular activities, including proliferation and survival among others. Gain-of-function missense mutations, mostly located at codons 12, 13, and 61, constitutively activate RAS proteins and can be detected in various types of human cancers. KRAS is the most frequently mutated, followed by NRAS and HRAS. However, each isoform exhibits distinctive mutation frequency at each codon, supporting the hypothesis that different RAS mutants may lead to distinct biologic manifestations. This review is focused on the differences in signaling and phenotype, as well as on transcriptomics, proteomics, and metabolomics profiles related to individual RAS-mutated variants. Additionally, association of these mutants with particular targeted outcomes and rare mutations at additional RAS codons are discussed.

Background An accumulation of evidence has revealed the important role of epigenetic factors in explaining the etiopathogenesis of human diseases. Several empirical studies have successfully incorporated methylation data into models for disease prediction. However, it is still a challenge to integrate different types of omics data into prediction models, and the contribution of methylation information to prediction remains to be fully clarified. Results A stratified drug-response prediction model was built based on an artificial neural network to predict the change in the circulating triglyceride level after fenofibrate intervention. Associated single-nucleotide polymorphisms (SNPs), methylation of selected cytosine-phosphate-guanine (CpG) sites, age, sex, and smoking status, were included as predictors. The model with selected SNPs achieved a mean 5-fold cross-validation prediction error rate of 43.65%. After adding methylation information into the model, the error rate dropped to 41.92%. The combination of significant SNPs, CpG sites, age, sex, and smoking status, achieved the lowest prediction error rate of 41.54%. Conclusions Compared to using SNP data only, adding methylation data in prediction models slightly improved the error rate; further prediction error reduction is achieved by a combination of genome, methylation genome, and environmental factors.