scholarly journals A Novel Inflammatory-Related Gene Signature Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

2022 ◽  
Vol 12 ◽  
Author(s):  
Wen-Yu Zhai ◽  
Fang-Fang Duan ◽  
Si Chen ◽  
Jun-Ye Wang ◽  
Yao-Bin Lin ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Survival Rates ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Individual Treatment ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Potential Biomarker

Inflammation is an important hallmark of cancer and plays a role in both neogenesis and tumor development. Despite this, inflammatory-related genes (IRGs) remain to be poorly studied in lung adenocarcinoma (LUAD). We aim to explore the prognostic value of IRGs for LUAD and construct an IRG-based prognosis signature. The transcriptomic profiles and clinicopathological information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression were applied in the TCGA set to generate an IRG risk signature. LUAD cases with from the GSE31210 and GSE30219 datasets were used to validate the predictive ability of the signature. Analysis of the TCGA cohort revealed a five-IRG risk signature consisting of EREG, GPC3, IL7R, LAMP3, and NMUR1. This signature was used to divide patients into two risk groups with different survival rates. Multivariate Cox regression analysis verified that the risk score from the five-IRG signature negatively correlated with patient outcome. A nomogram was developed using the IRG risk signature and stage, with C-index values of 0.687 (95% CI: 0.644–0.730) in the TCGA training cohort, 0.678 (95% CI: 0.586–0.771) in GSE30219 cohort, and 0.656 (95% CI: 0.571–0.740) in GSE30219 cohort. Calibration curves were consistent between the actual and the predicted overall survival. The immune infiltration analysis in the TCGA training cohort and two GEO validation cohorts showed a distinctly differentiated immune cell infiltration landscape between the two risk groups. The IRG risk signature for LUAD can be used to predict patient prognosis and guide individual treatment. This risk signature is also a potential biomarker of immunotherapy.

scholarly journals Six-long Non-coding RNA Signature to Predict the Prognosis of Lung Adenocarcinoma

2020 ◽  
Author(s):  
Yang Wang ◽  
Chengping Hu
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Protein Coding ◽  
Cox Regression Analysis ◽  
Protein Coding Genes

Abstract Background: Long non-coding RNAs (lncRNAs) have been reported to play essential roles in tumorigenesis and cancers prognosis, and they can be a potential cancer prognostic markers. However, in lung adenocarcinoma(LUAD), how lncRNA signatures predict the survival of patients is poorly understood. Our study aims to explore lncRNA signatures and prognostic function in LUAD.Methods: The expression and prognosis data of lncRNAs in LUAD patients was collected from the Cancer Genome Atlas (TCGA) data. All analyses were performed using the R package (version 3.6.2). Metascape, STRING and Cytoscape were used for enrichment analysis and function prediction of the lncRNA co-expressed protein-coding genes.Results: We have collected lncRNA expression data in 466 LUAD tumors, and a six-lncRNA signature(RP11-79H23.3, RP11-309M7.1, CTD-2357A8.3, RP11-108P20.4, U47924.29, LHFPL3-AS2) has been shown to be significantly related to LUAD patients’ overall survival. According to the lncRNA signatures, the high-risk and low-risk groups were divided in LUAD patients with different survival rates. Further multivariable cox regression analysis showed that the prognostic value of this signature was independent of clinical factors. The potential functional roles and hub co-expressed protein-coding genes in the six prognostic lncRNAs are shown in the functional enrichment analysis.Conclusions: These results showed that these six lncRNAs could be independent predicted prognostic biomarkers in LUAD patients.

scholarly journals An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Qian Xu ◽  
Yurong Chen
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Staging System ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Net Benefit ◽  
Cox Regression Analysis

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

scholarly journals A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Aisha Al-Dherasi ◽  
Qi-Tian Huang ◽  
Yuwei Liao ◽  
Sultan Al-Mosaib ◽  
Rulin Hua ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Training Group ◽  
The Cancer Genome Atlas ◽  
Individual Treatment ◽  
Significant Implication ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. Methods Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. Results A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis. Conclusion Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

scholarly journals Development of a Gene Signature Associated with Iron Metabolism in Lung Adenocarcinoma

2021 ◽  
Author(s):  
Junqi Qin ◽  
Zhanyu Xu ◽  
Fanglu Qin ◽  
Jiangbo Wei ◽  
Liqiang Yuan ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background: There are few studies on the role of iron metabolism genes in predicting the prognosis of lung adenocarcinoma (LUAD). Our research aims to screen key genes and to establish a prognostic signature that can predict the overall survival rate of lung adenocarcinoma patients. Methods: Genes related to iron metabolism were downloaded from the GeneCards database; in addition, RNA-Seq data and corresponding clinical materials of 594 adenocarcinoma patients from The Cancer Genome Atlas(TCGA) were downloaded. GSE42127 of Gene Expression Omnibus (GEO) database was also further verified. The multi-gene prognostic signature was constructed by the Cox regression model of the Least Absolute Shrinkage and Selection Operator (LASSO). The clinical applicability of the model and its connection with immune cell infiltration was then analyzed. Results: We constructed a prediction signature with 12 genes (HAVCR1, SPN, GAPDH, ANGPTL4, PRSS3, KRT8, LDHA, HMMR, SLC2A1, CYP24A1, LOXL2, TIMP1) in the TCGA test set, and counted the patient's risk value based on this 12-gene signature; patients were split into high and low-risk groups. The survival graph results revealed that the survival prognosis between the high and low-risk groups was significantly different (TCGA: P <0.001, GEO: P = 0.001). Univariate and multivariate Cox regression analysis confirmed that the risk value is a predictor of patient OS (P<0.001). The area under the time-dependent ROC curve (AUC) indicated that our signature had a relatively high true positive rate when predicting the 1-year, 3-year, and 5-year OS of the TCGA cohort, which was 0.735, 0.711, and 0.601, respectively. The analysis of the nomogram and calibration curve showed the predictive ability of the gene model. In addition, immune-related pathways were highlighted in the functional enrichment analysis, and immune response between the two risk groups was observed to be significantly different. All of the results proved the reliability of our iron metabolism-related gene risk prognostic model. Conclusion: We developed and verified a 12-gene prognostic signature, which can help predict the prognosis of lung adenocarcinoma and offer a variety of targeted options for the precise treatment of lung cancer.

scholarly journals PAFAH1B3 Predicts Poor Prognosis and Promotes Progression in Lung Adenocarcinoma

2021 ◽  
Author(s):  
Suping Tang ◽  
Jun Ni ◽  
Bohua Chen ◽  
Fei Sun ◽  
Songshi Ni ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Risk Factor ◽  
Western Blot ◽  
Lung Adenocarcinoma ◽  
Tissue Microarray ◽  
Immune Cell ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Abstract Background Recently, increasing evidence has indicated that platelet-activating factor acetylhydrolase 1b catalytic subunit 3 (PAFAH1B3) plays an important role in several cancers. However, the role in lung adenocarcinoma (LUAD) has not been reported until now. Methods Expression of PAFAH1B3 in LUAD was determined by Gene Expression Profiling Interactive Analysis (GEPIA), real-time PCR (RT-PCR), Western blot and Immunohistochemical (IHC) analysis. LUAD datasets with clinical information were obtained from The Cancer Genome Atlas Program (TCGA). Chi-square test was used to investigate the correlation between PAFAH1B3 expression and clinical parameters. Cox regression and Kaplan-Meier analysis were performed to analyzed the prognostic value of PAFAH1B3. CCK-8 assay, clone formation assay, transwell invasion assay and flow cytometry were conducted to detect cell proliferation, clone formation, invasion and cell cycle. Western blot was performed to detect epithelial-to-mesenchymal transition (EMT)-related markers. Immune Cell Abundance Identifier (ImmuneCellAI) was used to analyze the effect of PAFAH1B3 on immune cell infiltration. Results Our study showed that PAFAH1B3 was upregulated in LUAD, and silencing PAFAH1B3 suppressed cell proliferation, colony formation, invasion and increased the cell population in G0-G1 phases in vitro. In addition, tissue microarray IHC analysis showed that the PAFAH1B3 protein level was remarkably correlated with distant metastasis, TNM stage and clinical outcome. Furthermore, multivariate cox regression analysis based on TCGA-LUAD datasets and tissue microarray indicated that PAFAH1B3 was an independent prognostic risk factor for LUAD patients. Moreover, knockdown of PAFAH1B3 inhibited EMT in LUAD cells and PAFAH1B3 mRNA expression was correlated with immune infiltrates. Conclusion Our studies indicate that PAFAH1B3, a prognostic risk factor, promotes proliferation, invasion and EMT and affects immune infiltrates in LUAD.

scholarly journals Genome-Wide Analysis of Prognostic Alternative Splicing Signature and Splicing Factors in Lung Adenocarcinoma

Genes ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1300
Author(s):  
Ya-Sian Chang ◽  
Siang-Jyun Tu ◽  
Hui-Shan Chiang ◽  
Ju-Chen Yen ◽  
Ya-Ting Lee ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
Splicing Factors ◽  
Cox Regression Analysis ◽  
Underlying Mechanisms

Analysis of The Cancer Genome Atlas data revealed that alternative splicing (AS) events could serve as prognostic biomarkers in various cancer types. This study examined lung adenocarcinoma (LUAD) tissues for AS and assessed AS events as potential indicators of prognosis in our cohort. RNA sequencing and bioinformatics analysis were performed. We used SUPPA2 to analyze the AS profiles. Using univariate Cox regression analysis, overall survival (OS)-related AS events were identified. Genes relating to the OS-related AS events were imported into Cytoscape, and the CytoHubba application was run. OS-related splicing factors (SFs) were explored using the log-rank test. The relationship between the percent spliced-in value of the OS-related AS events and SF expression was identified by Spearman correlation analysis. We found 1957 OS-related AS events in 1151 genes, and most were protective factors. Alternative first exon splicing was the most frequent type of splicing event. The hub genes in the gene network of the OS-related AS events were FBXW11, FBXL5, KCTD7, UBB and CDC27. The area under the curve of the MIX prediction model was 0.847 for 5-year survival based on seven OS-related AS events. Overexpression of SFs CELF2 and SRSF5 was associated with better OS. We constructed a correlation network between SFs and OS-related AS events. In conclusion, we identified prognostic predictors using AS events that stratified LUAD patients into high- and low-risk groups. The discovery of the splicing networks in this study provides an insight into the underlying mechanisms.

scholarly journals A Novel Six Autophagy-Related Genes Signature Associated With Outcomes and Immune Microenvironment in Lower-Grade Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Lin ◽  
Hao Cheng ◽  
Da Liu ◽  
Lei Wen ◽  
Junlin Kang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Lower Grade ◽  
Immune Microenvironment ◽  
Cox Regression Analysis ◽  
Significant Difference

Since autophagy and the immune microenvironment are deeply involved in the tumor development and progression of Lower-grade gliomas (LGG), our study aimed to construct an autophagy-related risk model for prognosis prediction and investigate the relationship between the immune microenvironment and risk signature in LGG. Therefore, we identified six autophagy-related genes (BAG1, PTK6, EEF2, PEA15, ITGA6, and MAP1LC3C) to build in the training cohort (n = 305 patients) and verify the prognostic model in the validation cohort (n = 128) and the whole cohort (n = 433), based on the data from The Cancer Genome Atlas (TCGA). The six-gene risk signature could divide LGG patients into high- and low-risk groups with distinct overall survival in multiple cohorts (all p &lt; 0.001). The prognostic effect was assessed by area under the time-dependent ROC (t-ROC) analysis in the training, validation, and whole cohorts, in which the AUC value at the survival time of 5 years was 0.837, 0.755, and 0.803, respectively. Cox regression analysis demonstrated that the risk model was an independent risk predictor of OS (HR &gt; 1, p &lt; 0.05). A nomogram including the traditional clinical parameters and risk signature was constructed, and t-ROC, C-index, and calibration curves confirmed its robust predictive capacity. KM analysis revealed a significant difference in the subgroup analyses’ survival. Functional enrichment analysis revealed that these autophagy-related signatures were mainly involved in the phagosome and immune-related pathways. Besides, we also found significant differences in immune cell infiltration and immunotherapy targets between risk groups. In conclusion, we built a powerful predictive signature and explored immune components (including immune cells and emerging immunotherapy targets) in LGG.

scholarly journals PAFAH1B3 predicts poor prognosis and promotes progression in lung adenocarcinoma

2021 ◽  
Author(s):  
Suping Tang ◽  
Jun Ni ◽  
Bohua Chen ◽  
Fei Sun ◽  
Songshi Ni ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Risk Factor ◽  
Western Blot ◽  
Lung Adenocarcinoma ◽  
Tissue Microarray ◽  
Immune Cell ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Abstract Background Recently, increasing evidence has indicated that platelet-activating factor acetylhydrolase 1b catalytic subunit 3 (PAFAH1B3) plays an important role in several cancers. However, the role in lung adenocarcinoma (LUAD) has not been reported until now. Methods Expression of PAFAH1B3 in LUAD was determined by Gene Expression Profiling Interactive Analysis (GEPIA), real-time PCR (RT-PCR), Western blot and Immunohistochemical (IHC) analysis. LUAD datasets with clinical information were obtained from The Cancer Genome Atlas Program (TCGA). Chi-square test was used to investigate the correlation between PAFAH1B3 expression and clinical parameters. Cox regression and Kaplan-Meier analysis were performed to analyzed the prognostic value of PAFAH1B3. CCK-8 assay, clone formation assay, transwell invasion assay and flow cytometry were conducted to detect cell proliferation, clone formation, invasion and cell cycle. Western blot was performed to detect epithelial-to-mesenchymal transition (EMT)-related markers. Immune Cell Abundance Identifier (ImmuneCellAI) was used to analyze the effect of PAFAH1B3 on immune cell infiltration. Results Our study showed that PAFAH1B3 was upregulated in LUAD, and silencing PAFAH1B3 suppressed cell proliferation, colony formation, invasion and increased the cell population in G0-G1 phases in vitro. In addition, tissue microarray IHC analysis showed that the PAFAH1B3 protein level was remarkably correlated with distant metastasis, TNM stage and clinical outcome. Furthermore, multivariate cox regression analysis based on TCGA-LUAD datasets and tissue microarray indicated that PAFAH1B3 was an independent prognostic risk factor for LUAD patients. Moreover, knockdown of PAFAH1B3 inhibited EMT in LUAD cells and PAFAH1B3 mRNA expression was correlated with immune infiltrates. Conclusion Our studies indicate that PAFAH1B3, a prognostic risk factor, promotes proliferation, invasion and EMT and affects immune infiltrates in LUAD.

scholarly journals A Prediction Model Using Alternative Splicing Events and the Immune Microenvironment Signature in Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Liping Zhu ◽  
Zhiqiang Wang ◽  
Yilan Sun ◽  
Georgios Giamas ◽  
Justin Stebbing ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Lung Adenocarcinoma ◽  
Regulatory Networks ◽  
Immune Cell ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Microenvironment ◽  
Cox Regression Analysis ◽  
Gene And Protein Expression

BackgroundAlternative splicing (AS) is a gene regulatory mechanism that drives protein diversity. Dysregulation of AS is thought to play an essential role in cancer initiation and development. This study aimed to construct a prognostic signature based on AS and explore the role in the tumor immune microenvironment (TIME) in lung adenocarcinoma.MethodsWe analyzed transcriptome profiling and clinical lung adenocarcinoma data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq. Prognosis-related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan–Meier survival analyses, and proportional hazards model. The context of TIME in lung adenocarcinoma was also analyzed. Gene and protein expression data of Cyclin-Dependent Kinase Inhibitor 2A (CDKN2A) were obtained from ONCOMINE and Human Protein Atlas. Splicing factor (SF) regulatory networks were visualized.ResultsA total of 19,054 survival-related AS events in lung adenocarcinoma were screened in 1,323 genes. Exon skip (ES) and mutually exclusive exons (ME) exhibited the most and fewest AS events, respectively. Based on AS subtypes, eight AS prognostic signatures were constructed. Patients with high-risk scores were associated with poor overall survival. A nomogram with good validity in prognostic prediction was generated. AUCs of risk scores at 1, 2, and 3 years were 0.775, 0.736, and 0.759, respectively. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. Low-risk patients had a higher StromalScore, ImmuneScore, and ESTIMATEScore. AS-based risk score signature was positively associated with CD8+ T cells. CDKN2A was also found to be a prognostic factor in lung adenocarcinoma. Finally, potential functions of SFs were determined by regulatory networks.ConclusionTaken together, our findings show a clear association between AS and immune cell infiltration events and patient outcome, which could provide a basis for the identification of novel markers and therapeutic targets for lung adenocarcinoma. SF networks provide information of regulatory mechanisms.

NLRP3 is a Novel Prognostic Biomarker and Correlates With Immune Infiltrates in Lung Adenocarcinoma and Skin Cutaneous Melanoma

2021 ◽  
Author(s):  
Chenxi Yuan ◽  
Qingwei Wang ◽  
Xueting Dai ◽  
Yipeng Song ◽  
Jinming Yu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Potential Biomarker ◽  
The Impact ◽  
Nlrp3 Expression

Abstract Background: Lung adenocarcinoma (LUAD) and skin cutaneous melanoma (SKCM) are common tumors around the world. However, the prognosis in advanced patients is poor. Because NLRP3 was not extensively studied in cancers, so that we aimed to identify the impact of NLRP3 on LUAD and SKCM through bioinformatics analyses. Methods: TCGA and TIMER database were utilized in this study. We compared the expression of NLRP3 in different cancers and evaluated its influence on survival of LUAD and SKCM patients. The correlations between clinical information and NLRP3 expression were analyzed using logistic regression. Clinicopathologic characteristics associated with overall survival in were analyzed by Cox regression. In addition, we explored the correlation between NLRP3 and immune infiltrates. GSEA and co-expressed gene with NLRP3 were also done in this study. Results: NLRP3 expressed disparately in tumor tissues and normal tissues. Cox regression analysis indicated that up-regulated NLRP3 was an independent prognostic factor for good prognosis in LUAD and SKCM. Logistic regression analysis showed increased NLRP3 expression was significantly correlated with favorable clinicopathologic parameters such as no lymph node invasion and no distant metastasis. Specifically, a positive correlation between increased NLRP3 expression and immune infiltrating level of various immune cells was observed. Conclusion: Together with all these findings, increased NLRP3 expression correlates with favorable prognosis and increased proportion of immune cells in LUAD and SKCM. These conclusions indicate that NLRP3 can serve as a potential biomarker for evaluating prognosis and immune infiltration level.

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