Identification of a 5-Gene-Based Scoring System by WGCNA and LASSO to Predict Prognosis for Rectal Cancer Patients

Background. Although accumulating evidence suggested that a molecular signature panel may be more effective for the prognosis prediction than routine clinical characteristics, current studies mainly focused on colorectal or colon cancers. No reports specifically focused on the signature panel for rectal cancers (RC). Our present study was aimed at developing a novel prognostic signature panel for RC. Methods. Sequencing (or microarray) data and clinicopathological details of patients with RC were retrieved from The Cancer Genome Atlas (TCGA-READ) or the Gene Expression Omnibus (GSE123390, GSE56699) database. A weighted gene coexpression network was used to identify RC-related modules. The least absolute shrinkage and selection operator analysis was performed to screen the prognostic signature panel. The prognostic performance of the risk score was evaluated by survival curve analyses. Functions of prognostic genes were predicted based on the interaction proteins and the correlation with tumor-infiltrating immune cells. The Human Protein Atlas (HPA) tool was utilized to validate the protein expression levels. Results. A total of 247 differentially expressed genes (DEGs) were commonly identified using TCGA and GSE123390 datasets. Brown and yellow modules (including 77 DEGs) were identified to be preserved for RC. Five DEGs (ASB2, GPR15, PRPH, RNASE7, and TCL1A) in these two modules constituted the optimal prognosis signature panel. Kaplan-Meier curve analysis showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. Receiver operating characteristic (ROC) curve analysis demonstrated that this risk score had high predictive accuracy for unfavorable prognosis, with the area under the ROC curve of 0.915 and 0.827 for TCGA and GSE56699 datasets, respectively. This five-mRNA classifier was an independent prognostic factor. Its predictive accuracy was also higher than all clinical factor models. A prognostic nomogram was developed by integrating the risk score and clinical factors, which showed the highest prognostic power. ASB2, PRPH, and GPR15/TCL1A were predicted to function by interacting with CASQ2/PDK4/EPHA67, PTN, and CXCL12, respectively. TCL1A and GPR15 influenced the infiltration levels of B cells and dendritic cells, while the expression of PRPH was positively associated with the abundance of macrophages. HPA analysis supported the downregulation of PRPH, RNASE7, CASQ2, EPHA6, and PDK4 in RC compared with normal controls. Conclusion. Our immune-related signature panel may be a promising prognostic indicator for RC.

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A Novel Ferroptosis-Related Long Non-coding RNA Prognostic Risk Model for Gastric Cancer

10.21203/rs.3.rs-718468/v1 ◽

2021 ◽

Author(s):

Shenglan Huang ◽

Dan Li ◽

Lingling Zhuang ◽

Liying Sun ◽

Jianbing Wu

Keyword(s):

Gastric Cancer ◽

Risk Score ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Clinical Stage ◽

Curve Analysis ◽

Clinicopathological Features ◽

Roc Curve Analysis ◽

Kaplan Meier

Abstract Background：Gastric cancer (GC) is one of the most common malignant tumors with a poor prognosis. Ferroptosis is a novel and distinct type of non-apoptotic cell death that is closely associated with metabolism, redox biology, and tumor prognosis. Recently, ferroptosis-related long non-coding RNAs (lncRNAs) have received increasing attention in predicting cancer prognosis. Thus, we aimed to construct an ferroptosis-related lncRNAs signature for predicting the prognosis of patients with gastric cancer.Methods：We built an ferroptosis-related lncRNA risk signature by using Cox regression based on TCGA database. Kaplan-Meier survival analysis was conducted to compare the overall survival (OS) in different risk groups. Cox regression was performed to explore whether the signature could be used as an independent factor. A nomogram was built involving the risk score and clinicopathological features. Furthermore, we explored the biological functions and immune states in two groups.Results：Eight ferroptosis-related lncRNAs were obtained for constructing the prognosis model in gastric cancer. Kaplan–Meier curve analysis revealed that patients in the high-risk group had worse survival than those in the low-risk group. The survival outcome was also appropriate for subgroup analysis, including age, sex, grade, and clinical stage. Multivariate Cox regression analysis and receiver operating characteristic (ROC) curve analysis demonstrated that the risk score was an independent prognostic factor and superior to traditional clinicopathological features in predicting GC prognosis. Next, we established a nomogram according to clinical parameters (age, sex, grade, and clinical stage) and risk score. All the verified results, including ROC curve analysis, calibration curve, and decision curve analysis, demonstrated that the nomogram could accurately predict the survival of patients with gastric cancer. Gene set enrichment analysis revealed that these lncRNAs were mainly involved in cell adhesion, cancer pathways, and immune function regulation.Conclusion: We established a novel ferroptosis-related prognostic risk signature including eight lncRNAs and constructed a nomogram to predict the prognosis of gastric cancer patients, which may improve prognostic predictive accuracy and guide individualized treatment for patients with GC.

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Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics

Frontiers in Genetics ◽

10.3389/fgene.2021.739470 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jiahui Pan ◽

Xinyue Zhang ◽

Xuedong Fang ◽

Zhuoyuan Xin

Keyword(s):

Gastric Cancer ◽

Cancer Patients ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Curve Analysis ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Gastric Cancer Patients

BackgroundGastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.MethodsFerroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan–Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).ResultsA ferroptosis-related “lncRNA–mRNA” co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.ConclusionOur prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.

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Identification of a Novel Eight-lncRNA Prognostic Signature for HBV-HCC and Analysis of Their Functions Based on Coexpression and ceRNA Networks

BioMed Research International ◽

10.1155/2020/8765461 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Xiaonan Zhao ◽

Zhenzi Bai ◽

Chenghua Li ◽

Chuanlun Sheng ◽

Hongyan Li

Keyword(s):

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Characteristic Curve ◽

The Cancer Genome Atlas ◽

Good Prediction ◽

Prognostic Signature ◽

Training Set ◽

Validation Set

Studies have demonstrated the prognosis potential of long noncoding RNAs (lncRNAs) for hepatocellular carcinoma (HCC), but specific lncRNAs for hepatitis B virus- (HBV-) related HCC have rarely been reported. This study was aimed at identifying a lncRNA prognostic signature for HBV-HCC and exploring their underlying functions. The sequencing dataset was collected from The Cancer Genome Atlas database as the training set, while the microarray dataset was obtained from the European Bioinformatics Institute database (E-TABM-36) as the validation set. Univariate and multivariate Cox regression analyses identified that eight lncRNAs (TSPEAR-AS1, LINC00511, LINC01136, MKLN1-AS, LINC00506, KRTAP5-AS1, ZNF252P-AS1, and THUMPD3-AS1) were significantly associated with overall survival (OS). These eight lncRNAs were used to construct a risk score model. The Kaplan-Meier survival curve results showed that this risk score can significantly differentiate the OS between the high-risk group and the low-risk group. Receiver operating characteristic curve analysis demonstrated that this risk score exhibited good prediction effectiveness (area under the curve AUC=0.990 for the training set; AUC=0.903 for the validation set). Furthermore, this lncRNA risk score was identified as an independent prognostic factor in the multivariate analysis after adjusting other clinical characteristics. The crucial coexpression (LINC00511-CABYR, THUMPD3-AS1-TRIP13, LINC01136-SFN, LINC00506-ANLN, and KRTAP5-AS1/TSPEAR-AS1/MKLN1-AS/ZNF252P-AS1-MC1R) or competing endogenous RNA (THUMPD3-AS1-hsa-miR-450a-TRIP13) interaction axes were identified to reveal the possible functions of lncRNAs. These genes were enriched into cell cycle-related biological processes or pathways. In conclusion, our study identified a novel eight-lncRNA prognosis signature for HBV-HCC patients and these lncRNAs may be potential therapeutic targets.

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Validation and Application of Prognostic Signature Based on Necroptosis-related Genes in Patients With Thyroid Carcinoma

10.21203/rs.3.rs-1060995/v1 ◽

2021 ◽

Author(s):

yiming tao ◽

Hang Ruan ◽

Hui Zhao ◽

Wenpei Dang ◽

Xinxin Xu ◽

...

Keyword(s):

Thyroid Carcinoma ◽

Cox Regression ◽

Predictive Accuracy ◽

Curve Analysis ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Prognostic Signature ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Clinical Variables

Abstract ObjectiveTo explore the relationship between thyroid carcinoma (TC) and necroptosis, and to construct a related prognostic signature to assist in diagnosis and treatment.Methods and ResultsA total of 159 necroptosis-related genes (NRGs) were screened for in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database; 38 differentially expressed NRGs (DENGs) of TC were identified from The Cancer Genome Atlas/Genomic Data Commons database (TCGA/GDC); and GO, KEGG, and GSEA enrichment analysis showed that they were mostly related to cell necrosis, autophagy, P53, and other signaling pathways. Univariate and multivariate Cox regression and Lasso regression were used to screen for DENGs associated with prognosis, and a prognostic signature about BID, H2AC12, STAT1, IFNA21, IL1A was established. The patients were then divided into high-risk and low-risk groups according to the median value of the prognostic signature, and their overall survival (OS) was analyzed via the Kaplan-Meier method. The predictive accuracy was also determined using receiver operating characteristic (ROC) curve analysis. Additionally, we performed stratification analyses based on different clinical variables and evaluated the correlations between risk score and clinical variables. The independent prognostic value of the signature was further confirmed by multivariate Cox regression analysis, and decision curve analysis (DCA) was employed to evaluate the quality of the prognostic model and its clinical utility.ConclusionWe successfully constructed a novel necroptosis-related signature for the prediction of prognosis in patients with TC.

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Mortality risk prediction in lupus patients complicated with invasive infection in the emergency department: LUPHAS score

Therapeutic Advances in Musculoskeletal Disease ◽

10.1177/1759720x19885559 ◽

2019 ◽

Vol 11 ◽

pp. 1759720X1988555 ◽

Cited By ~ 1

Author(s):

Wanlong Wu ◽

Jun Ma ◽

Yuhong Zhou ◽

Chao Tang ◽

Feng Zhao ◽

...

Keyword(s):

Emergency Department ◽

Prediction Model ◽

Risk Score ◽

Roc Curve ◽

Curve Analysis ◽

Multivariable Logistic Regression Analysis ◽

Invasive Infection ◽

Roc Curve Analysis ◽

All Cause Mortality ◽

Qsofa Score

Background: Infection remains a major cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE). This study aimed to establish a clinical prediction model for the 3-month all-cause mortality of invasive infection events in patients with SLE in the emergency department. Methods: SLE patients complicated with invasive infection admitted into the emergency department were included in this study. Patient’s demographic, clinical, and laboratory characteristics on admission were retrospectively collected as baseline data and compared between the deceased and the survivors. Independent predictors were identified by multivariable logistic regression analysis. A prediction model for all-cause mortality was established and evaluated by receiver operating characteristic (ROC) curve analysis. Results: A total of 130 eligible patients were collected with a cumulative 38.5% 3-month mortality. Lymphocyte count <800/ul, urea >7.6mmol/l, maximum prednisone dose in the past ⩾60 mg/d, quick Sequential Organ Failure Assessment (qSOFA) score, and age at baseline were independent predictors for all-cause mortality (LUPHAS). In contrast, a history of hydroxychloroquine use was protective. In a combined, odds ratio-weighted LUPHAS scoring system (score 3–22), patients were categorized to three groups: low-risk (score 3–9), medium-risk (score 10–15), and high-risk (score 16–22), with mortalities of 4.9% (2/41), 45.9% (28/61), and 78.3% (18/23) respectively. ROC curve analysis indicated that a LUPHAS score could effectively predict all-cause mortality [area under the curve (AUC) = 0.86, CI 95% 0.79–0.92]. In addition, LUPHAS score performed better than the qSOFA score alone (AUC = 0.69, CI 95% 0.59–0.78), or CURB-65 score (AUC = 0.69, CI 95% 0.59–0.80) in the subgroup of lung infections ( n = 108). Conclusions: Based on a large emergency cohort of lupus patients complicated with invasive infection, the LUPHAS score was established to predict the short-term all-cause mortality, which could be a promising applicable tool for risk stratification in clinical practice.

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Use of percent positive biopsy cores to predict prostate cancer–specific death in patients treated with dose-escalated radiotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2011.29.7_suppl.35 ◽

2011 ◽

Vol 29 (7_suppl) ◽

pp. 35-35

Author(s):

Y. Qian ◽

F. Y. Feng ◽

S. Halverson ◽

K. Blas ◽

H. M. Sandler ◽

...

Keyword(s):

Prostate Cancer ◽

Roc Curve ◽

Risk Group ◽

Curve Analysis ◽

External Beam Radiation ◽

Rank Test ◽

Roc Curve Analysis ◽

Cut Point ◽

Log Rank Test ◽

Positive Biopsy

35 Background: The percent of positive biopsy cores (PPC)-considered a surrogate of local disease burden-has been shown to predict biochemical failure (BF) after external beam radiation therapy (EBRT), but most series have used conventional dose RT. Dose-escalated RT has been demonstrated to improve prostate cancer outcomes, but the value of PPC is unclear in the setting of RT doses high enough to decrease local failure. Methods: A retrospective evaluation was performed of 651 patients treated to ≥75 Gy with biopsy core information available. Patients were stratified for PPC by quartile, and differences by quartile in BF, freedom from metastasis (FFM), cause specific survival (CSS), and overall survival (OS) were assessed using the log-rank test. Receiver operated characteristic (ROC) curve analysis was utilized to determine an optimal cut-point for PPC. Cox proportional hazards multivariate regression was utilized to assess the impact of PPC on clinical outcome when adjusting for risk group. Results: With median follow-up of 62 months the median number of cores sampled was 7 (IQR: 6–12) with median PPC in 38% (IQR: 17%-67%). On log-rank test, BF, FFM, and CSS were all associated with PPC (p < 0.005 for all), with worse outcomes only for the highest PPC quartile (>67%). There was no observed difference in OS based upon PPC. ROC curve analysis confirmed a cut-point of 67% as most closely associated with CSS (p<0.001, AUC=0.71). On multivariate analysis after adjusting for NCCN risk group and ADT use, PPC>67% increased the risk for BF (p<0.0001, HR:2.1 [1.4–3.0]), FFM (p<0.05, HR:1.7 [1.1 to 2.9]), and CSS (p<0.06 (HR:2.1 [1.0–4.6]). When analyzed as a continuous variable controlling for risk group and ADT use, increasing PPC increased the risk for BF (p < 0.002), metastasis (p < 0.05), and CSS (p < 0.02), with a 1–2% increase in relative risk of recurrence for each 1% increase in the PPC. Conclusions: For patients treated with dose-escalated RT, the PPC adds prognostic value but at a higher cut-point then previously utilized. Patients with PPC >67% remain at increased risk for failure even with dose-escalated EBRT and may receive benefit from further intensification of therapy. No significant financial relationships to disclose.

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Modified N Stage of Esophageal Cancer Based on the Evaluation of the Hezode Rate of the Negative and Positive Lymph Node

10.21203/rs.3.rs-32922/v1 ◽

2020 ◽

Author(s):

Jinling Zhang ◽

Hongyan Li ◽

Liangjian Zhou ◽

lianling Yu ◽

Fengyuan Che ◽

...

Keyword(s):

Esophageal Cancer ◽

Lymph Node ◽

Roc Curve ◽

Cross Validation ◽

Predictive Accuracy ◽

Positive Lymph Node ◽

Curve Analysis ◽

Roc Curve Analysis ◽

Validation Method ◽

N Stage

Abstract Objective:The study aimed to propose a modified N stage of esophageal cancer (EC) on basis of based on the number of positive lymph node (PLN) and the number of negative lymph node (NLN) simultaneously. Method:Data from 13,491 patients with EC registered in the SEER database were reviewed. The parameters related to prognosis were investigated using a Cox proportional hazards regression model. A modified N stage was proposed based on the cut-off number of the re-adjusted ratio of the number of PLN (numberPLN) to the number of NLN (numberNLN), which derived from the comparison of the hezode rate (HR) of numberPLN and numberNLN. The modified N stage was confirmed using the cross-validation method with the training and validation cohort, and it was also compared to the N stage from the American Joint Committee on Cancer (AJCC) staging system (7th edition) using Receiver Operating Characteristic (ROC) curve analysis.Results:The numberPLN on prognosis was 1.042, while numberNLN was 0.968. The modified N stage was defined as follows: N1 stage: the ratio range was from 0 to 0.21; N2 stage: more than 0.21, but no more than 0.48; N3 stage: more than 0.48. Cross-validation method within the cohort identified the predictive accuracy of this modified N stage, and ROC curve analysis demonstrated the superiority of this modified N stage over that of the AJCC.Conclusion:The modified N stage based on the re-adjusted ratio of numberPLN to numberNLN can evaluate tumor stage more accurately than the traditional N stage.

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An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

BioMedical Engineering OnLine ◽

10.1186/s12938-021-00902-7 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Shanshan Tang ◽

Yiyi Zhuge

Keyword(s):

Cancer Treatment ◽

Risk Score ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Prognostic Significance ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Multiple Cancer

Abstract Background Pseudogenes show multiple functions in various cancer types, and immunotherapy is a promising cancer treatment. Therefore, this study aims to identify immune-related pseudogene signature in endometrial cancer (EC). Methods Gene transcriptome data of EC tissues and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) through UCSC Xena browser. Spearman correlation analysis was performed to identify immune-related pseudogenes (IRPs) between the immune genes and pseudogenes. Univariate Cox regression, LASSO, and multivariate were performed to develop a risk score signature to investigate the different overall survival (OS) between high- and low-risk groups. The prognostic significance of the signature was assessed by the Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC) curve. The abundance of 22 immune cell subtypes of EC patients was evaluated using CIBERSORT. Results Nine IRPs were used to build a prognostic signature. Survival analysis revealed that patients in the low-risk group presented longer OS than those in the high-risk group as well as in multiple subgroups. The signature risk score was independent of other clinical covariates and was associated with several clinicopathological variables. The prognostic signature reflected infiltration by multiple types of immune cells and revealed the immunotherapy response of patients with anti-programmed death-1 (PD-1) and anti-programmed cell death 1 ligand 1 (PD-L1) therapy. Function enrichment analysis revealed that the nine IRPs were mainly involved in multiple cancer-related pathways. Conclusion We identified an immune-related pseudogene signature that was strongly correlated with the prognosis and immune response to EC. The signature might have important implications for improving the clinical survival of EC patients and provide new strategies for cancer treatment.

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The role of peroxisome proliferator-activated receptor gamma and adiponectin in children with Kawasaki disease

Journal of International Medical Research ◽

10.1177/0300060521994925 ◽

2021 ◽

Vol 49 (3) ◽

pp. 030006052199492

Author(s):

Ji-Yong Zhang ◽

Hong Peng ◽

Si-Tang Gong ◽

Yong-Mei Zeng ◽

Miao Huang ◽

...

Keyword(s):

Kawasaki Disease ◽

Roc Curve ◽

Predictive Accuracy ◽

Mrna Level ◽

Curve Analysis ◽

Peroxisome Proliferator ◽

Mrna Levels ◽

Roc Curve Analysis ◽

Peroxisome Proliferator Activated Receptor ◽

Paediatric Patients

Objective To investigate the relationship between peroxisome proliferator-activated receptor gamma (PPARγ) mRNA, serum adiponectin (ADP) and lipids in paediatric patients with Kawasaki disease (KD). Methods This prospective study enrolled paediatric patients with KD and grouped them according to the presence or absence of coronary artery lesions (CAL). A group of healthy age-matched children were recruited as the control group. The levels of PPARγ mRNA, serum ADP and lipids were compared between the groups. Receiver operating characteristic (ROC) curve analysis was undertaken to determine if the PPARγ mRNA level could be used as a predictive biomarker of CAL prognosis. Results The study enrolled 42 patients with KD (18 with CAL [CAL group] and 24 without CAL [NCAL group]) and 20 age-matched controls. PPARγ mRNA levels in patients with KD were significantly higher than those in the controls; but significantly lower in the CAL group than the NCAL group. ROC curve analysis demonstrated that the PPARγ mRNA level provided good predictive accuracy for the prognosis of CAL. There was no association between PPARγ, ADP and lipid levels. Conclusion There was dyslipidaemia in children with KD, but there was no correlation with PPARγ and ADP. PPARγ may be a predictor of CAL in patients with KD with good predictive accuracy.

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Identification of Cross-Talk and Pyroptosis-Related Genes Linking Periodontitis and Rheumatoid Arthritis Revealed by Transcriptomic Analysis

Disease Markers ◽

10.1155/2021/5074305 ◽

2021 ◽

Vol 2021 ◽

pp. 1-29

Author(s):

Yongbin Jing ◽

Dong Han ◽

Chunyang Xi ◽

Jinglong Yan ◽

Jinpeng Zhuang

Keyword(s):

Rheumatoid Arthritis ◽

Cross Talk ◽

Roc Curve ◽

Predictive Accuracy ◽

Curve Analysis ◽

Differentially Expressed ◽

Roc Curve Analysis ◽

The Core ◽

The Cross ◽

Blue Module

Background. The current study is aimed at identifying the cross-talk genes between periodontitis (PD) and rheumatoid arthritis (RA), as well as the potential relationship between cross-talk genes and pyroptosis-related genes. Methods. Datasets for the PD (GSE106090, GSE10334, GSE16134) and RA (GSE55235, GSE55457, GSE77298, and GSE1919) were downloaded from the GEO database. After batch correction and normalization of datasets, differential expression analysis was performed to identify the differentially expressed genes (DEGs). The cross-talk genes linking PD and RA were obtained by overlapping the DEGs dysregulated in PD and DEGs dysregulated in RA. Genes involved in pyroptosis were summarized by reviewing literatures, and the correlation between pyroptosis genes and cross-talk genes was investigated by Pearson correlation coefficient. Furthermore, the weighted gene coexpression network analysis (WGCNA) was carried out to identify the significant modules which contained both cross-talk genes and pyroptosis genes in both PD data and RA data. Thus, the core cross-talk genes were identified from the significant modules. Receiver-operating characteristic (ROC) curve analysis was performed to identify the predictive accuracy of these core cross-talk genes in diagnosing PD and RA. Based on the core cross-talk genes, the experimentally validated protein-protein interaction (PPI) and gene-pathway network were constructed. Results. A total of 40 cross-talk genes were obtained. Most of the pyroptosis genes were not differentially expressed in disease and normal samples. By selecting the modules containing both cross-talk genes or pyroptosis genes, the blue module was identified to be significant module. Three genes, i.e., cross-talk genes (TIMP1, LGALS1) and pyroptosis gene-GPX4, existed in the blue module of PD network, while two genes (i.e., cross-talk gene-VOPP1 and pyroptosis gene-AIM2) existed in the blue module of RA network. ROC curve analysis showed that three genes (TIMP1, VOPP1, and AIM2) had better predictive accuracy in diagnosing disease compared with the other two genes (LGALS1 and GPX4). Conclusions. This study revealed shared mechanisms between RA and PD based on cross-talk and pyroptosis genes, supporting the relationship between the two diseases. Thereby, five modular genes (TIMP1, LGALS1, GPX4, VOPP1, and AIM2) could be of relevance and might serve as potential biomarkers. These findings are a basis for future research in the field.

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