Dataset Information

Immune phenotype predicts risk for posttransplantation squamous cell carcinoma.

ABSTRACT: Cutaneous squamous cell cancer (SCC) affects up to 30% of kidney transplant recipients (KTRs) within 10 years of transplantation. There are no reliable clinical tests that predict those who will develop multiple skin cancers. High numbers of regulatory T cells associate with poor prognosis for patients with cancer in the general population, suggesting their potential as a predictive marker of cutaneous SCC in KTRs. We matched KTRs with (n = 65) and without (n = 51) cutaneous SCC for gender, age, and duration of immunosuppression and assessed several risk factors for incident SCC during a median follow-up of 340 days. Greater than 35 peripheral FOXP3(+)CD4(+)CD127(low) regulatory T cells/microl, <100 natural killer cells/microl, and previous SCC each significantly associated with increased risk for new cutaneous SCC development (hazard ratio [HR] 2.48 [95% confidence interval (CI) 1.04 to 5.98], HR 5.6 [95% CI 1.31 to 24], and HR 1.33 [95% CI 1.15 to 1.53], respectively). In addition, the ratio of CD8/FOXP3 expression was significantly lower in cutaneous SCC excised from KTRs (n = 25) compared with matched SCC from non-KTRs (n = 25) and associated with development of new cutaneous SCCs. In summary, monitoring components of the immune system can predict development of cutaneous SCC among KTRs.

SUBMITTER: Carroll RP

PROVIDER: S-EPMC2844307 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Immune phenotype predicts risk for posttransplantation squamous cell carcinoma.

Carroll Robert P RP Segundo David San DS Hollowood Kevin K Marafioti Teresa T Clark Taane G TG Harden Paul N PN Wood Kathryn J KJ

Journal of the American Society of Nephrology : JASN 20100128 4

Cutaneous squamous cell cancer (SCC) affects up to 30% of kidney transplant recipients (KTRs) within 10 years of transplantation. There are no reliable clinical tests that predict those who will develop multiple skin cancers. High numbers of regulatory T cells associate with poor prognosis for patients with cancer in the general population, suggesting their potential as a predictive marker of cutaneous SCC in KTRs. We matched KTRs with (n = 65) and without (n = 51) cutaneous SCC for gender, age, ...[more]

PMID: 20110382

Similar Datasets

Project description:Objective: Immunity plays a vital role in the human papilloma virus (HPV) persistent infection, and closely associates with occurrence and development of cervical squamous cell carcinoma (CSCC). Herein, we performed an integrated bioinformatics analysis to establish an immune-gene signature and immune-associated nomogram for predicting prognosis of CSCC patients. Methods: The list of immunity-associated genes was retrieved from ImmPort database. The gene and clinical information of CSCC patients were obtained from The Cancer Genome Atlas (TCGA) website. The immune gene signature for predicting overall survival (OS) of CSCC patients was constructed using the univariate Cox-regression analysis, random survival forests, and multivariate Cox-regression analysis. This signature was externally validated in GSE44001 cohort from Gene Expression Omnibus (GEO). Then, based on the established signature and the TCGA cohort with the corresponding clinical information, a nomogram was constructed and evaluated via Cox regression analysis, concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots and decision curve analyses (DCAs). Results: A 5-immune-gene prognostic signature for CSCC was established. Low expression of ICOS, ISG20 and high expression of ANGPTL4, SBDS, LTBR were risk factors for CSCC prognosis indicating poor OS. Based on this signature, the OS was significantly worse in high-risk group than in low-risk group (p-value < 0.001), the area under curves (AUCs) for 1-, 3-, 5-years OS were, respectively, 0.784, 0.727, and 0.715. A nomogram incorporating the risk score of signature and the clinical stage was constructed. The C-index of this nomogram was 0.76. AUC values were 0.811, 0.717, and 0.712 for 1-, 3-, 5-years OS. The nomogram showed good calibration and gained more net benefits than the 5-immune-gene signature and the clinical stage. Conclusion: The 5-immune-gene signature may serve as a novel, independent predictor for prognosis in patients with CSCC. The nomogram incorporating the signature risk score and clinical stage improved the predictive performance than the signature and clinical stage alone for predicting 1-year OS.

Project description:ObjectiveRecurrence remains the main cause of the poor prognosis in stage I-IIIA lung squamous cell carcinoma (LUSC) after surgical resection. In the present study, we aimed to identify the long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) related to the recurrence of stage I-IIIA LUSC. Moreover, we constructed a risk assessment model to predict the recurrence of LUSC patients.MethodsRNA sequencing data (including miRNAs, lncRNAs, and mRNAs) and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed lncRNAs, miRNAs, and mRNAs were identified using the "DESeq2" package of the R language. Univariate Cox proportional hazards regression analysis and Kaplan-Meier curve were used to identify recurrence-related genes. Stepwise multivariate Cox regression analysis was carried out to establish a risk model for predicting recurrence in the training cohort. Moreover, Kaplan-Meier curves and receiver operating characteristic (ROC) curves were adopted to examine the predictive performance of the signature in the training cohort, validation cohort, and entire cohort.ResultsBased on the TCGA database, we analyzed the differentially expressed genes (DEGs) among 27 patients with recurrent stage I-IIIA LUSC and 134 patients with non-recurrent stage I-IIIA LUSC, and identified 431 lncRNAs, 36 miRNAs, and 746 mRNAs with different expression levels. Out of these DEGs, the optimal combination of DEGs was finally determined, and a nine-joint RNA molecular signature was constructed for clinical prediction of recurrence, including LINC02683, AC244517.5, LINC02418, LINC01322, AC011468.3, hsa-mir-6825, AC020637.1, AC027117.2, and SERPINB12. The ROC curve proved that the model had good predictive performance in predicting recurrence. The area under the curve (AUC) of the prognostic model for recurrence-free survival (RFS) was 0.989 at 3 years and 0.958 at 5 years (in the training set). The combined RNA signature also revealed good predictive performance in predicting the recurrence in the validation cohort and entire cohort.ConclusionsIn the present study, we constructed a nine-joint RNA molecular signature for recurrence prediction of stage I-IIIA LUSC. Collectively, our findings provided new and valuable clinical evidence for predicting the recurrence and targeted treatment of stage I-IIIA LUSC.

Dataset Information

Immune phenotype predicts risk for posttransplantation squamous cell carcinoma.

Publications

Immune phenotype predicts risk for posttransplantation squamous cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure