Dataset Information

High serum C-reactive protein levels predict survival in patients with treated advanced lung adenocarcinoma.

ABSTRACT:

Background

The prognosis of non-small cell lung cancer (NSCLC) varies greatly depending on whether or not it can receive molecular-targeted drug treatment including epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). We investigated the clinical utility of C-reactive protein (CRP) levels measured at the time of diagnosis in EGFR-mutant and wild-type NSCLC patients who had undergone first-line therapy.

Methods

Serum CRP levels were analyzed in 213 patients, of whom 89 patients had advanced EGFR-mutated NSCLC who underwent first-line EGFR-TKI treatment. We used Cox proportional hazards models to study the relationship between CRP and overall survival (OS). CRP cutoff values were obtained from the receiver operating characteristic curve.

Results

Mean serum CRP level in treated NSCLC patients were not significantly different in patients with or without EGFR mutations. The optimal CRP cutoff values were 8.1 mg/L for EGFR-mutated NSCLC and 16.7 mg/L for EGFR-wild NSCLC. Based on multivariate analysis, high CRP level (EGFR-mutated, HR: 2.479, 95% CI: 1.331-4.619, P=0.004; EGFR-wild, HR: 3.625, 95% CI: 2.149-6.116, P<0.001) was a significant and independent negative prognostic factor for OS in patients with or without EGFR mutations.

Conclusions

High CRP levels predicted a lack of response to treatment in patients with advanced lung adenocarcinoma with or without EGFR mutations. Thus, the CRP level is a good and easy to use prognostic factor and objective indicator for clinical practice.

SUBMITTER: Hotta T

PROVIDER: S-EPMC8024849 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

High serum C-reactive protein levels predict survival in patients with treated advanced lung adenocarcinoma.

Hotta Takamasa T Nakashima Kazuhisa K Hata Kojiro K Tsubata Yukari Y Isobe Takeshi T

Journal of thoracic disease 20210301 3

<h4>Background</h4>The prognosis of non-small cell lung cancer (NSCLC) varies greatly depending on whether or not it can receive molecular-targeted drug treatment including epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). We investigated the clinical utility of C-reactive protein (CRP) levels measured at the time of diagnosis in EGFR-mutant and wild-type NSCLC patients who had undergone first-line therapy.<h4>Methods</h4>Serum CRP levels were analyzed in 213 patients, o ...[more]

PMID: 33841940

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Project description:BackgroundLung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis.MethodThe information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters.ResultA total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year.ConclusionA series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

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Dataset Information

High serum C-reactive protein levels predict survival in patients with treated advanced lung adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

High serum C-reactive protein levels predict survival in patients with treated advanced lung adenocarcinoma.

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