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Transcription profiling of human NSCLC samples to predict recurrence-free survival in postoperative nsclc patients

ABSTRACT: Background:; One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results:; The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good results of the proposed model (p=0.0314, 0.0305, respectively). The predicted median recurrence-free survival times for each patient were reflected real ones well. Conclusions:; Our method of individualized recurrence risk prediction is accurate, technically simple and reproducible to be used in clinical practice. Therefore, it would be useful in customizing the lung cancer management strategies. Experiment Overall Design: Methods: Experiment Overall Design: At first, we selected the statistically significant genes from the analysis of time-to-recurrence and censoring information from 138 whole-genome wide microarray data. Then, we further reduced the number of genes which could be reliably reproducible by RTQ-PCR. With these assayable genes and clinical parameters, construction of recurrence prediction model by Cox proportional hazard regression was done. After validation into two independent cohorts (n=59 and n=56), the model was transformed into recurrence prediction for the each patient.

ORGANISM(S): Homo sapiens

SUBMITTER: Kim Jinkook

PROVIDER: S-ECPF-GEOD-8894 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:Background: It is urgent to develop robust prognostic biomarkers for non-metastatic colorectal cancer (CRC) patients undergoing surgery. The current study aimed to explore and compare the clinical significance of preoperative and postoperative blood tumor biomarkers including circulating tumor cells (CTCs), and develop prognostic models based on tumor biomarkers in patients with stage II-III CRC receiving surgery. Methods: A prospective study was performed to enroll 130 patients with stage II-III CRC receiving surgery between January 2015 and December 2017. Preoperative and postoperative blood tumor biomarkers including CTCs were detected and their prognostic value in predicting tumor recurrence-free survival (RFS) in stage II-III CRC were identified by Kaplan-Meier curves and Cox proportional hazard regression models. Results: CTCs counts within three postoperative days were significantly higher than preoperative CTCs (pre-CTCs). No significant association of pre-CTCs with clinical characteristics and tumor biomarkers was observed while positive postoperative CTCs (post-CTCs) were associated with female, older onset age, high TNM stage, tumor recurrence, and preoperative CEA. Kaplan-Meier curve with log-rank test and univariate Cox proportional hazard regression analysis suggested high N stage, TNM stage, positive pre-carbohydrate antigen (CA) 125, pre-CA19-9, post-CA125, post-CA19-9, post-CA72-4, post-carcinoembryonic antigen (CEA), and post-CTCs were correlated with poor RFS. In multivariate analysis, only TNM stage (adjusted HR=3.786, 95% CI=1.330-10.780; P=0.013), post-CA72-4 (adjusted HR=5.675, 95% CI=2.064-15.604; P=0.001), and post-CTCs (adjusted HR=2.739, 95% CI=1.042-7.200; P=0.041) were significantly correlated with poor RFS. We then developed prognostic models combining post-CTCs and post-CA72-4 with TNM stage or not to stratify the patients into different risk groups. These prognostic models exert a similar good performance in predicting tumor RFS in stage II-III CRC patients. Conclusions: Postoperative CTCs were prior to preoperative CTCs in predicting tumor recurrence survival in non-metastatic CRC patients undergoing surgery. We also developed CTCs-based prognostic models to predict tumor recurrence in stage II-III CRC, which might be used to identify the patients with high risk of recurrence and guide aggressive treatment to improve the clinical outcomes of those patients.

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Transcription profiling of human NSCLC samples to predict recurrence-free survival in postoperative nsclc patients

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