Publications by authors named "Chuandi Jin"

4 Publications

  • Page 1 of 1

Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study.

Front Genet 2021 3;12:625145. Epub 2021 Jun 3.

Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China.

Ovarian serous cancer (OSC) is one of the leading causes of death across the world. The role of the tumor microenvironment (TME) in OSC has received increasing attention. Targeted maximum likelihood estimation (TMLE) is developed under a counterfactual framework to produce effect estimation for both the population level and individual level. In this study, we aim to identify TME-related genes and using the TMLE method to estimate their effects on the 3-year mortality of OSC. In total, 285 OSC patients from the TCGA database constituted the studying population. ESTIMATE algorithm was implemented to evaluate immune and stromal components in TME. Differential analysis between high-score and low-score groups regarding ImmuneScore and StromalScore was performed to select shared differential expressed genes (DEGs). Univariate logistic regression analysis was followed to evaluate associations between DEGs and clinical pathologic factors with 3-year mortality. TMLE analysis was conducted to estimate the average effect (AE), individual effect (IE), and marginal odds ratio (MOR). The validation was performed using three datasets from Gene Expression Omnibus (GEO) database. Additionally, 355 DEGs were selected after differential analysis, and 12 genes from DEGs were significant after univariate logistic regression. Four genes remained significant after TMLE analysis. In specific, ARID3C and FREM2 were negatively correlated with OSC 3-year mortality. CROCC2 and PTF1A were positively correlated with OSC 3-year mortality. Combining of ESTIMATE algorithm and TMLE algorithm, we identified four TME-related genes in OSC. AEs were estimated to provide averaged effects based on the population level, while IEs were estimated to provide individualized effects and may be helpful for precision medicine.
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http://dx.doi.org/10.3389/fgene.2021.625145DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211425PMC
June 2021

Identification of microenvironment related potential biomarkers of biochemical recurrence at 3 years after prostatectomy in prostate adenocarcinoma.

Aging (Albany NY) 2021 06 16;13(12):16024-16042. Epub 2021 Jun 16.

Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China.

Prostate adenocarcinoma is one of the leading adult malignancies. Identification of multiple causative biomarkers is necessary and helpful for determining the occurrence and prognosis of prostate adenocarcinoma. We aimed to identify the potential prognostic genes in the prostate adenocarcinoma microenvironment and to estimate the causal effects simultaneously. We obtained the gene expression data of prostate adenocarcinoma from TCGA project and identified the differentially expressed genes based on immune-stromal components. Among these genes, 68 were associated with biochemical recurrence at 3 years after prostatectomy in prostate adenocarcinoma. After adjusting for the minimal sets of confounding covariates, 14 genes (, and ) related to the microenvironment were identified as prognostic biomarkers using the targeted maximum likelihood estimation. Both the average and individual causal effects were obtained to measure the magnitude of the effect. CIBERSORT and gene set enrichment analyses showed that these prognostic genes were mainly associated with immune responses. and were correlated with androgen receptor expression, a main driver of prostate adenocarcinoma progression. Finally, five genes were validated in another prostate adenocarcinoma cohort (GEO: GSE70770). These findings might lead to the improved prognosis of prostate adenocarcinoma.
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http://dx.doi.org/10.18632/aging.203121DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266350PMC
June 2021

Causal Effects of Overall and Abdominal Obesity on Insulin Resistance and the Risk of Type 2 Diabetes Mellitus: A Two-Sample Mendelian Randomization Study.

Front Genet 2020 2;11:603. Epub 2020 Jul 2.

Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China.

Overall and abdominal obesity were significantly associated with insulin resistance and type 2 diabetes mellitus (T2DM) risk in observational studies, though these associations cannot avoid the bias induced by confounding effects and reverse causation. This study aimed to test whether these associations are causal, and it compared the causal effects of overall and abdominal obesity on T2DM risk and glycemic traits by using a two-sample Mendelian randomization (MR) design. Based on summary-level statistics from genome-wide association studies, the instrumental variables for body mass index (BMI), waist-to-hip ratio (WHR), and WHR adjusted for BMI (WHRadjBMI) were extracted, and the horizontal pleiotropy was analyzed using MR-Egger regression and the MR-pleiotropy residual sum and outlier (PRESSO) method. Thereafter, by using the conventional MR method, the inverse-variance weighted method was applied to assess the causal effect of BMI, WHR, and WHRadjBMI on T2DM risk, Homeostatic model assessment of insulin resistance (HOMA-IR), fasting insulin, fasting glucose, and Hemoglobin A1c (HbA1c). A series of sensitivity analyses, including the multivariable MR (diastolic blood pressure, systolic blood pressure, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol as covariates), MR-Egger regression, weighted median, MR-PRESSO, and leave-one-out method, were conducted to test the robustness of the results from the conventional MR. Despite the existence of horizontal pleiotropy, consistent results were found in the conventional MR results and sensitivity analyses, except for the association between BMI and fasting glucose, and WHRadjBMI and fasting glucose. Each one standard deviation higher BMI was associated with an increased T2DM risk [odds ratio (OR): 2.741; 95% confidence interval (CI): 2.421-3.104], higher HbA1c [1.054; 1.04-1.068], fasting insulin [1.202; 1.173-1.231], and HOMA-IR [1.221; 1.187-1.255], similar to findings for causal effect of WHRadjBMI on T2DM risk [1.993; 1.704-2.33], HbA1c [1.061; 1.042-1.08], fasting insulin [1.102; 1.068-1.136], and HOMA-IR [1.127; 1.088-1.167]. Both BMI ( = 0.546) and WHRadjBMI ( = 0.443) were unassociated with fasting glucose in the multivariable MR analysis. In conclusion, overall and abdominal obesity have causal effects on T2DM risk and insulin resistance but no causal effect on fasting glucose. Individuals can substantially reduce their insulin resistance and T2DM risk through reduction of body fat mass and modification of body fat distribution.
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http://dx.doi.org/10.3389/fgene.2020.00603DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7343715PMC
July 2020

Identification of gene biomarkers in patients with postmenopausal osteoporosis.

Mol Med Rep 2019 Feb 12;19(2):1065-1073. Epub 2018 Dec 12.

Department of Research and Development, Gu'an Bojian Bio‑Technology Co., Ltd., Langfang, Hebei 065000, P.R. China.

Postmenopausal osteoporosis (PMOP) is a major public health concern worldwide. The present study aimed to provide evidence to assist in the development of specific novel biomarkers for PMOP. Differentially expressed genes (DEGs) were identified between PMOP and normal controls by integrated microarray analyses of the Gene Expression Omnibus (GEO) database, and the optimal diagnostic gene biomarkers for PMOP were identified with LASSO and Boruta algorithms. Classification models, including support vector machine (SVM), decision tree and random forests models, were established to test the diagnostic value of identified gene biomarkers for PMOP. Functional annotations and protein‑protein interaction (PPI) network constructions were also conducted. Integrated microarray analyses (GSE56815, GSE13850 and GSE7429) of the GEO database were employed, and 1,320 DEGs were identified between PMOP and normal controls. An 11‑gene combination was also identified as an optimal biomarker for PMOP by feature selection and classification methods using SVM, decision tree and random forest models. This combination was comprised of the following genes: Dehydrogenase E1 and transketolase domain containing 1 (DHTKD1), osteoclast stimulating factor 1 (OSTF1), G protein‑coupled receptor 116 (GPR116), BCL2 interacting killer, adrenoceptor β1 (ADRB1), neogenin 1 (NEO1), RB binding protein 4 (RBBP4), GPR87, cylicin 2, EF‑hand calcium binding domain 1 and DEAH‑box helicase 35. RBBP4 (degree=12) was revealed to be the hub gene of this PMOP‑specific PPI network. Among these 11 genes, three genes (OSTF1, ADRB1 and NEO1) were speculated to serve roles in PMOP by regulating the balance between bone formation and bone resorption, while two genes (GPR87 and GPR116) may be involved in PMOP by regulating the nuclear factor‑κB signaling pathway. Furthermore, DHTKD1 and RBBP4 may be involved in PMOP by regulating mitochondrial dysfunction and interacting with ESR1, respectively. In conclusion, the findings of the current study provided an insight for exploring the mechanism and developing novel biomarkers for PMOP. Further studies are required to test the diagnostic value for PMOP prior to use in a clinical setting.
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http://dx.doi.org/10.3892/mmr.2018.9752DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323213PMC
February 2019
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