Genetics research最新文献

The key event of liver regeneration initiation (LRI) is the switch of hepatocytes from the G0 phase to the G1 phase. This study aimed to use the data from large-scale quantitatively detecting and analyzing (LQDA) to reveal the regulation of hepatocytes in the G0 or G1 phase by competing endogenous RNAs (ceRNAs) during LRI. The hepatocytes of the rat liver right lobe were isolated 0, 6, and 24 h after partial hepatectomy. Their ceRNA expression level was measured using LQDA, and the correlation among their expression, interaction, and role was revealed by ceRNA comprehensive analysis. The expression of neurogenic loci notch homologous protein 3 (NOTCH3) mRNA was upregulated in 0 h, but the expression of miR-369-3p and rno-Rmdn2_0006 of hepatocytes did not change significantly. Meanwhile, the expression of the G0 phase-related gene CDKN1c was promoted by NOTCH3 upregulation, and the expression of the G1 phase-related gene PSEN2 was inhibited by NOTCH3 downregulation. On the contrary, the expression of NOTCH3 mRNA and rno-Rmdn2_0006 was upregulated at 6 h, but the expression of miR-136-3p was downregulated. The expression of the G1 phase-related genes CHUK, DDX24, HES1, NET1, and STAT3 was promoted by NOTCH3 upregulation, and the expression of the G0 phase-related gene CDKN1a was inhibited by NOTCH3 downregulation. These results suggested that the ceRNAs and the NOTCH3-regulated G0 phase- and G1 phase-related genes showed a correlation in expression, interaction, and role. They together regulated the hepatocytes in the G0 phase at 0 h and in the G1 phase at 6 h. These findings might help understand the mechanism by which ceRNA together regulated the hepatocytes in the G0 or G1 phase.

Background: The role of disulfidptosis-related lncRNAs remains unclear in lung adenocarcinoma.

Methods: Analysis in R software was conducted using different R packages, which are based on the public data from The Cancer Genome Atlas (TCGA) database. The transwell assay was used to evaluate the invasion and migration abilities of lung cancer cells.

Results: In our study, we identified 1401 lncRNAs significantly correlated with disulfidptosis-related genes (|Cor| > 0.3 and P < 0.05). Then, we constructed a prognosis model consisting of 11 disulfidptosis-related lncRNAs, including AL133445.2, AL442125.1, AC091132.2, AC090948.1, AC020765.2, CASC8, AL606834.1, LINC00707, OGFRP1, U91328.1, and GASAL1. This prognosis model has satisfactory prediction performance. Also, the risk score and clinical information were combined to develop a nomogram. Analyses of biological enrichment and immune-related data were used to identify underlying differences between patients at high-risk and low-risk groups. Moreover, we noticed that the immunotherapy nonresponders have higher risk scores. Meanwhile, patients at a high risk responded more strongly to docetaxel, paclitaxel, and vinblastine. Furthermore, further analysis of the model lncRNA OGFRP1 was conducted, including clinical, immune infiltration, biological enrichment analysis, and a transwell assay. We discovered that by inhibiting OGFRP1, the invasion and migration abilities of lung cancer cells could be remarkably hindered.

Conclusion: The results of our study can provide directions for future research in the relevant areas. Moreover, the prognosis signature we identified has the potential for clinical application.

Background: Cytochrome P450 complex plays a key role in drug metabolism. CYP2B6 has an essential part in Cytochrome P450 complex metabolism. This study aims to determine the allelic distribution of CYP2B6^∗2 and CYP2B6^∗3 in three main Iranian ethnicities: Fars, Turk, and Kurd.

Methods: The study was conducted on 174 unrelated healthy volunteers from three main Iranian ethnicities. After DNA extraction from peripheral blood samples, genotyping of CYP2B6^∗2 and ^∗3 was performed using tetra ARMS and ARMS PCR, respectively.

Results: The average age of 174 cases was 40.69 ± 11.87 (mean ± SD) and 39.06 ± 11.63 (mean ± SD) for males and females. In the CYP2B6^∗2 variant, the genotyping frequency of wild type (C/C), heterozygous (C/T), and homozygous mutant (T/T) was 8.7%, 86%, and 5.2%, respectively. The CYP2B6^∗2 (c.64C > T) allele frequency was 48.2% (95% CI: (37.8-58.6)). In the CYP2B6^∗3 variant, the frequency of wild type (C/C), heterozygous (C/T), and homozygous mutant (T/T) was 75.3%, 11%, and 13.6%, respectively. The CYP2B6^∗3 (c.777C > A) allelic frequency was 19.1% (95% CI: (17.5-20.7)).

Conclusion: Allelic distribution in three main Iranian ethnicities, i.e., Turk, Kurd, and Fars, is remarkably higher than that in other populations, even that in Southern Iran. High frequencies of CYP2B6^∗2 and ^∗3 in the Iranian population highly affect drug responsiveness. Understanding such variability could help to increase drug efficacy and reduce its toxicity.

On a global scale, renal cell carcinoma (RCC) is the second most common form of cancer and the 10th leading cause of cancer-related deaths. There are about 70% of cases of RCC that are clear cell renal cell carcinomas (ccRCCs). This study explores possible targets for immune therapy in patients with RCC. In the recent years, immunotherapy has been applied to RCC patients. In order to identify genes that are closely associated with immune cells, a weighted gene coexpression network analysis (WGCNA) was conducted. A close association was found between genes involved in MEred and M0 macrophages, M1 macrophages, and M2 macrophages. A prognostic prediction model is subsequently developed by incorporating the OS and the expression level of key genes from the RCC cohort into a univariate COX regression analysis, a multivariate COX regression analysis, and a combined COX regression analysis. We finally discovered that 6 genes are closely associated with the prognosis of RCC patients, including SLC16A12, SLC2A9, IGF2BP2, EMX2, ANK3, and METTL7A. The survival analysis proved the prognostic prediction value of the model. The 1-year, 3-year, and 5-year AUC of ROC curves are 0.759, 0.723, and 0.733, respectively. For clinical ROC curves, the AUC score for risk score, stage, grade, and T stage is 0.759, 0.824, 0722, and 0.736, respectively. The nomogram was constructed for better prognosis prediction of RCC patients. In addition, GSVA and GO enrichment analysis was performed to explore the potential pathways that are closely associated with genes involved in the prognostic prediction model. Accordingly, our study demonstrates that immune cells play a crucial role in RCC infiltration. The development of a prognostic prediction model is a potential new prognostic biomarker and potential immunotherapy target for tumors.

Chinese herbal medicine (CHM), which includes herbal slices and proprietary products, is widely used in China. Shenqi Dihuang (SQDH) is a traditional Chinese medicine (TCM) formula with ingredients that affect tumor growth. Despite recent advances in prognosis, patients with renal cell carcinoma (RCC) cannot currently receive curative treatment. The present study aimed to explore the potential target genes closely associated with SQDH. The gene expression data for SQDH and RCC were obtained from the TCMSP and TCGA databases. The SQDH-based prognostic prediction model reveals a strong correlation between RCC and SQDH. In addition, the immune cell infiltration analysis indicated that SQDH might be associated with the immune response of RCC patients. Based on this, we successfully built the prognostic prediction model using SQDH-related genes. The results demonstrated that CCND1 and NR3C2 are closely associated with the prognosis of RCC patients. Finally, the pathways enrichment analysis revealed that response to oxidative stress, cyclin binding, programmed cell death, and immune response are the most enriched pathways in CCND1. Furthermore, transcription regulator activity, regulation of cell population proliferation, and cyclin binding are closely associated with the NR3C2.

Objective: The purpose of our work was to explore the association of mutations in the androgen receptor gene and copy numbers of the androgen-receptor silk protein A complex with glutathione-S-transferases T1 and M1 in prostate cancer patients.

Materials and methods: Eighty-five patients with PC and 85 healthy controls were included in the study. Fasting peripheral venous blood was collected, whole blood genomic DNA was extracted, and AR gene-receptor genotype was detected by a high-resolution melting curve analysis detection technology. Expression levels of androgen receptor (AR) and filamin protein A (FlnA) were detected by Western blotting. RT-PCR was used to detect the copy number of T1 and M1 glutathione-S-transferases.

Results: The wild-type androgen receptor gene rs5918762 is of TT type. The frequencies of CC and TC genes in the prostate cancer group were significantly higher than those in the normal control group (P < 0.05). Compared with TT-type PC patients, PC patients with TC-type and CC-type had higher expression levels of sex hormone receptor silk protein A complex and higher copy numbers of GSTT1 and GSTM1 (P < 0.05). Androgen-receptor gene mutation (T ⟶ C) was significantly positively correlated with the expression level of androgen-receptor silk protein A complex and the copy number of GSTT1 and GSTM1.

Conclusion: Androgen-receptor gene polymorphisms were significantly associated with expression levels of androgen receptor complex A and silk proteins, and copy numbers of T1 and M1 glutathione-S-transferases. A combination of four factors can be used to identify prostate cancer susceptibility and disease progression.

Recurrent pregnancy loss (RPL) is both mental and physical health problem affecting about 1-5% of women of childbearing age. The etiology of RPL is complex, involving chromosomal abnormalities, autoimmune diseases, metabolic disorders, and endometrial dysfunction. The causes of abortion are still unknown in more than 50% of these cases. With the development of science and technology, an increasing number of scholars focus on this field and find that genetic factors may play an essential role in unexplained RPL, such as embolism-related genes, immune factor-related genes, and chromosomal numeric, and structural variation. This review summarizes the genetic factors associated with RPL, including genetic mutations and genetic polymorphisms, chromosomal variants, and chromosomal polymorphisms. Many related genetic factors have been found to be demographically and geographically relevant, some of which can be used for risk prediction or screening for the etiology of RPL. However, it is difficult to predict and prevent RPL due to uncertain pathogenesis and highly variable clinical presentation. Therefore, the genetic factors of RPL still need plentiful research to obtain a more accurate understanding of its pathogenesis and to provide more detection means for the screening and prevention of RPL.

Background: Complex regulation exists between tumor metabolism and M2 macrophages. Long noncoding RNAs (lncRNAs) are famous for their wide regulatory role. This study aimed to identify the lncRNAs involved in the crosstalk between tumor metabolism and M2 macrophages.

Methods: The Cancer Genome Atlas was responsible for the public data. R software was responsible for the analysis of public data.

Results: Based on the input expression profile, we quantified the M2 macrophage infiltration using the CIBERSORT algorithm and found that M2 macrophages were a risk factor for lung cancer. Also, we found that M2 macrophages were correlated with multiple metabolism pathways. Then, 67 lncRNAs involved in both M2 macrophages and related metabolism pathways were identified. A prognosis signature based on AC027288.3, AP001189.3, FAM30A, GAPLINC, LINC00578, and LINC01936 was established, which had good prognosis prediction ability. The clinical parameters and risk score were combined into a nomogram plot for better prediction of the patient's prognosis. A high fit of actual survival and nomogram-predicted survival was found using the calibration plot. Moreover, in low-risk patients, immunotherapy was more effective, while cisplatin and docetaxel were more effective in high-risk patients. Biological enrichment analysis indicated pathways of notch signaling, TGF-β signaling, interferon alpha response, and interferon-gamma response were activated in the high-risk group. Meanwhile, the risk score was associated with tumor metabolism and M2 macrophages. Also, we found that the promoting effect of CAPLINC on M2 macrophage polarization might act through multiple metabolism pathways.

Conclusions: Our result can provide new insights into the interaction between M2 macrophages and tumor metabolism, as well as the involved lncRNAs, which can provide the direction for future studies.

Osteoarthritis (OA) is a chronic musculoskeletal disease affecting approximately 500 million people worldwide. Globally, OA is one of the most common and leading causes of disability. Several genetic factors are involved in OA, including inherited genes, genetic susceptibility, and genetic predisposition. As the pathogenesis of OA is unknown, there are almost no effective treatments available to prevent the onset or progression of the disease. In recent years, many researchers focused on bioinformatics analysis to explore new biomarkers for the diagnosis, treatment, and prognosis of human diseases. In this work, we obtain the traditional RNA sequencing data of OA patients from the GEO database. By performing the differentially expressed analysis, we successfully obtain the genes that are closely associated with the OA. In addition, the Venn diagram was applied to evaluate the genes that are involved in OA and immune-related genes. The protein-protein interaction analysis was further conducted to explore the hub genes. The single-cell RNA sequencing analysis was used to evaluate the expression distribution of the MMP, VEGFA, SPI1, and IRF8 in synovial tissues of patients with osteoarthritis. Finally, the GSVA enrichment analysis discovered the potential pathways involved in OA patients. Our analysis provides a new direction for the exploration of the process of OA patients. In addition, VEGFA may be considered a promising biomarker in OA.

Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD.

Methods: RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465.

Results: A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues.

Conclusion: Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.