Genomic and immunogenomic analysis of three prognostic signature genes in LUAD.

BACKGROUND: Searching for immunotherapy-related markers is an important research content to screen for target populations suitable for immunotherapy. Prognosis-related genes in early stage lung cancer may also affect the tumor immune microenvironment, which in turn affects immunotherapy. RESULTS: We analyzed the differential genes affecting lung cancer patients receiving immunotherapy through the Cancer Treatment Response gene signature DataBase (CTR-DB), and set a threshold to obtain a total of 176 differential genes between response and non-response to immunotherapy. Functional enrichment analysis found that these differential genes were mainly involved in immune regulation-related pathways. The early-stage lung adenocarcinoma (LUAD) prognostic model was constructed through the cancer genome atlas (TCGA) database, and three target genes (MMP12, NFE2, HOXC8) were screened to calculate the risk score of early-stage LUAD. The receiver operating characteristic (ROC) curve indicated that the model had good prognostic value, and the validation set (GSE50081, GSE11969 and GSE42127) from the gene expression omnibus (GEO) analysis indicated that the model had good stability, and the risk score was correlated with immune infiltrations to varying degrees. Multi-type survival analysis and immune infiltration analysis revealed that the transcriptome, methylation and the copy number variation (CNV) levels of the three genes were correlated with patient prognosis and some tumor microenvironment (TME) components. Drug sensitivity analysis found that the three genes may affect some anti-tumor drugs. The mRNA expression of immune checkpoint-related genes showed significant differences between the high and low group of the three genes, and there may be a mutual regulatory network between immune checkpoint-related genes and target genes. Tumor immune dysfunction and exclusion (TIDE) analysis found that three genes were associated with immunotherapy response and maybe the potential predictors to immunotherapy, consistent with the CTR-DB database analysis. CONCLUSIONS: From the perspective of data mining, this study suggests that MMP12, NFE2, and HOXC8 may be involved in tumor immune regulation and affect immunotherapy. They are expected to become markers of immunotherapy and are worthy of further experimental research.

Identification of the Signature Genes and Network of Reactive Oxygen Species Related Genes and DNA Repair Genes in Lung Adenocarcinoma.

Reactive Oxygen Species (ROS) are present in excess amounts in patients with tumors, and these ROS can kill and destroy tumor cells. Therefore, tumor cells upregulate ROS-related genes to protect them and reduce their destructing effects. Cancer cells already damaged by ROS can be repaired by expressing DNA repair genes consequently promoting their proliferation. The present study aimed to identify the signature genes of and regulating network of ROS-related genes and DNA repair genes in lung adenocarcinoma (LUAD) using transcriptomic data of public databases. The LUAD transcriptome data in the TCGA database and gene expressions from Gene Expression Omnibus (GEO) were analyzed and samples were clustered into 5 ROS-related categories and 6 DNA repair categories. Survival analysis revealed a significant difference in patient survival between the two classification methods. In addition, the samples corresponding to the two categories overlap, thus, the gene expression profile of the same sample with different categories and survival prognosis was further explored, and the connection between ROS-related and DNA repair genes was investigated. The interactive sample recombination classification was used, revealing that the patient's prognosis was worse when the ROS-related and DNA repair genes were expressed at the same time. The further research on the potential regulatory network of the two categories of genes and the correlation analysis revealed that ROS-related genes and DNA repair genes have a mutual regulatory relationship. The ROS-related genes namely NQO1, TXNRD1, and PRDX4 could establish links with other DNA repair genes through the DNA repair gene NEIL3, thereby balancing the level of ROS. Therefore, targeting ROS-related genes and DNA repair genes might be a promising strategy in the treatment of LUAD. Finally, a survival prognostic model of ROS-related genes and DNA repair genes was established (TERT, PRKDC, PTTG1, SMUG1, TXNRD1, CAT, H2AFX, and PFKP). The risk score obtained from our survival prognostic model could be used as an independent prognostic factor in LUAD patients.

Identification of Signature Genes and Characterizations of Tumor Immune Microenvironment and Tumor Purity in Lung Adenocarcinoma Based on Machine Learning.

The implication of the Estimation of Stromal and Immune cells in Malignant tumor tissues using expression data (ESTIMATE) method to determine the tumor microenvironment (TME) and tumor immune score including tumor purity represents an efficient method to identify and assess biomarkers for immunotherapy response in precision medicine. In this study we utilized a machine learning algorithm to analyze the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) lung adenocarcinoma (LUAD) transcriptome data to evaluate the association between TME and tumor purity. Furthermore, we investigated whether fewer TME components or a few dominant genes can infer tumor purity. The results indicated that the 29 immune infiltrating components determined by the ssGSEA method could screen the 5 TME components [chemokine C-C-Motif receptor (CCR), T-helper-cells, Check-point, Treg, and tumor-infiltrating lymphocytes (TIL)] that significantly contributed the most to tumor purity prediction through regression tree and random forest regression methods. The findings revealed that higher activity of these five immune infiltrating components significantly lowered the tumor purity. Moreover, 5 TME components contributed significantly to the improvement of Mean Square Error (MES); therefore, we selected these five sets' genes and analyzed survival data to establish a prognostic model. We screened out 11 prognostic-related genes and constructed a risk model comprising 11 genes with good predictive value for patients' prognosis. Furthermore, we obtained four genes (GIMAP6, CD80, IL16, and CCR2) that had predictive advantages for tumor purity using random forest classification and random forest regression. The comprehensive score of genes for tumor purity prediction (CSGTPP) was obtained by least absolute shrinkage and selection operator (LASSO) regression indicated that four genes could be successfully used to classify high and low CSGTPP samples and that tumor purity was negatively correlated with CSGTPP. Survival analysis revealed that the higher the CSGTPP, the better the prognosis of patients. The association between a cluster of differentiation 274 (CD274) and CSGTPP revealed a higher expression of CD274 in the high CSGTPP group. Collectively, we speculated that CSGTPP could serve as a predictor of the response to immunotherapy and a promising indicator of immunotherapy effect.

[The Comparison of Iron Deficiency Anemia Rat Treated with Hydroxypropyl Chitosan Ferrous Ion Complex and Ferrous Sulfate].

OBJECTIVE: To observe and compare the therapeutic effects of hydroxypropyl chitosan ferrous ion complex solution and ferrous sulfate solution in iron deficiency anemia rats and their effects on gastric mucosa. METHODS: Seven rats were randomly selected from thirty five SPF grade SD rats as control group, and were fed with normal diet, distilled water (E). The rest of SD rats were fed with low iron feed and distilled water plus continuous tail vein bloodletting to establish the iron deficiency anemia model. After the model was established successfully, the rats were randomly divided into four groups: blank control group (A), iron deficiency anemia control group (B), ferrous sulfate group (C), hydroxypropyl chitosan ferrous ion complex (HPCTS-Fe2+) group (D). Group A was killed and the gastric tissue was taken to make the pathological section. Group E was fed with normal feed and distilled water continually. Group B, C and D were fed with low iron feed and distilled water. Moreover, Group C and group D were treated with ferrous sulfate solution and HPCTS-Fe2+ solution respectively to made the blood recover treatment (4 mg / kg of iron each time, twice a day). During the treatment period, Ret%, SF and blood routine were checked regularly. After 6 weeks of treatment, the rats were killed, and the gastric tissue was taken to make the pathological section, and the condition of gastric mucosa was observed by the light microscope. RESULTS: After modeling, except the normal control group, the hair color of the rats in the four groups showed dark yellow and the belly of the toes became white gradually. HGB, HCT, Ret%, MCV, MCH, MCHC and SF decreased significantly (P < 0.05). After treatment, the rats with dark yellow hair in group C and D were improved, and the toe abdomen turned pink gradually. RBC, HGB, HCT, Ret%, MCV, MCH, MCHC and SF in rats in group C and D increased, which were higher than those in group B (P < 0.05). The HGB of the rats in group D was higher than that of group C in day 28th during treatment and the Ret% was higher than that in group C at day 10th (P<0.05).After treatment, the liver and spleen of the rats in group C and D were lighter than those in group B (P<0.05).The gastric mucosa in group A, B, D and E was not damaged obviously, while it was slightly irritated and damaged in group C. CONCLUSION: Hydroxypropyl chitosan ferrous complex solution can improve the hemoglobin level of SD rats with iron deficiency anemia, which is stronger than ferrous sulfate solution and shows no damage to gastric mucosa.

MiR-199a targeting ROCK1 to affect kidney cell proliferation, invasion and apoptosis.

Renal cell carcinoma (RCC) is one of the three most common cancers of urinary tract cancer, accounting for 2-3% of all systemic cancers. Recent studies have found that miR-199a is lowly expressed in RCC, may act as a tumour suppressor gene to induce the occurrence of kidney cancer. In the present study, we investigated the role of miR-199a in the progression and metastasis of RCC. The results showed that miR-199a significantly downregulated in RCC and cell lines. Overexpression of miR-199a in RCC cell lines significantly inhibited cell proliferation, migration and invasion. Furthermore, the qRT-PCR and western blot results showed that miR-199a overexpression significantly downregulated ROCK-1 mRNA and protein levels. ROCK1 was identified as a target of miR-199a, and ectopic expression of miR-199a downregulated ROCK1 by direct binding to its 3' untranslated region. Together, these findings indicate that miR-199a acts as a tumour suppressor and its downregulation in tumour tissues may contribute to the progression and metastasis of RCC through a mechanism involving ROCK1, suggesting miR-199a as a potential new diagnostic and therapeutic target for the treatment of RCC.

[Association of adiponectin gene polymorphism with obesity in children].

OBJECTIVE: To study the distribution characteristics of adiponectin gene +45 single nucleotide polymorphisms (SNP) in Chinese children, and to determine the role of adiponectin gene +45 polymorphisms in the pathogenesis of childhood obesity. METHODS: A total of 147 Chinese obese and 118 healthy children were randomly selected and enrolled to identify adiponectin gene SNP+45 polymorphism by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. Plasma adiponectin levels were determined using ELISA. Waist circumference (WC), waist to hip ratio (WHR), percentage of body fat (%BF), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), serum triglycerides (TG), total cholesterol (TC), high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), plasma fasting insulin (FINS), and homeostasis model assessment of insulin resistance (HOMA-IR) were measured. RESULTS: The allelic frequency of adiponectin gene SNP+45 in children with obesity and healthy controls were 40.5% and 25.4%, respectively. There were significant differences in the distribution of genotypes and the allelic frequency between the two groups (P<0.05). The plasma adiponectin levels were significantly higher, in contrast, %BF, HOMA-IR, TC and LDL-C levels were significantly lower in obese children with TT genotype than those in obese children with TG or GG genotype. CONCLUSIONS: The adiponectin gene SNP+45 polymorphism may be associated with pathogenesis of obesity in children. T→G variance may be associated an increased risk of childhood obesity and result in a decreased level of adiponectin.

[Plasma levels of adiponectin and tumor necrosis factor-alpha in children with obesity].

OBJECTIVE: To examine plasma adiponectin (ADPN) and tumor necrosis factor-alpha (TNF-alpha) levels and their correlation in children with obesity in order to investigate the roles of both in the development of childhood obesity. METHODS: One hundred and forty-seven children with obesity and 118 normal children who were randomly sampled from five primary schools from the Kaifu District in Changsha were enrolled. Physical shape indexes, including height, weight, waist circumference, hip circumference, and waist to hip ratio (WHR) were measured. Body mass index (BMI) was calculated. Blood pressure was measured. Percentage of body fat (%BF) was measured with dual energy X-ray absorptiometry. Plasmal levels of ADPN and TNF-alpha were detected using ABC-ELISA. Blood concentrations of triglycerides (TG), total cholesterol (TC), high density lipoprotein-cholesterol (HDL-C) and low density lipoprotein-cholesterol (LDL-C) were measured by automatic biochemistry analyzer. Fasting blood glucose level was measured by glucose oxidase method. Fasting blood insulin level was assayed by radioimmunity. Homeostasis model assessment for insulin resistance (HOMA-IR) was performed. RESULTS: Plasma ADPN levels in obese children significantly decreased compared with those in normal children (8.12+/-2.54 mg/L vs 12.22+/-4.68 mg/L; p<0.05), and had a negative correlation with plasma TNF-alpha levels, BMI, WHR and HOMA-IR (p<0.01), and with %BF, fasting insulin, systolic blood pressure and TG (p<0.05). Plasma TNF-alpha levels in obese children significantly increased compared to normal children (171.38+/-34.33 ng/L vs 91.07+/-21.60 ng/L; p<0.01) and positively correlated with BMI, WHR, %BF, fasting insulin, HOMA-IR, TG and systolic blood pressure (p<0.01), and negatively with HDL (p<0.05). Multiple stepwise regression analysis showed that ADPN, BMI and TNF-alpha were main influential factors for %BF (R2=0.926, p<0.01). There was a significant interaction between ADPN and TNF-alpha (p<0.05). CONCLUSIONS: Plasma ADPN levels decreased and plasma TNF-alpha levels increased in children with obesity and both were main influential factors for %BF in children. There was an interaction between ADPN and TNF-alpha, suggesting that they both participate in the development of childhood obesity.