Dataset Information

P4HA1: A single-gene surrogate of hypoxia signatures in oral squamous cell carcinoma patients.

ABSTRACT: Background and purpose:Hypoxia gene expression signatures are of high prognostic value for head and neck cancer patients. Recently, the prognostic information of a multiple-gene hypoxia signature was found to be provided by the mRNA level of P4HA1 alone (Tawk et al., 2016). Therefore, we studied the prognostic value of P4HA1 in an independent cohort of oral squamous cell carcinoma (OSCC) patients. Material and methods:Frozen tumor samples of 118 adult OSCC patients were analysed for P4HA1 mRNA level by quantitative real-time TaqMan™ PCR analysis. Kaplan-Meier analysis and Cox's regression analysis were performed to characterize the prognostic impact of P4HA1 mRNA level in OSCC patients. Results:The analyzed patient cohort was divided into four subgroups according to the quartiles of the P4HA1 mRNA levels. The highest intratumoral P4HA1 mRNA level was significantly correlated with a poor overall survival (RR = 2.2; P = 0.04) and an increased risk of locoregional recurrence (RR = 4.8; P = 0.02). In patients who received radiotherapy (n = 82) highest intratumoral P4HA1 mRNA level was significantly correlated with a poor overall survival (RR = 3.4; P = 0.01) and an increased risk of locoregional recurrence (RR = 10.3; P = 0.005). Moreover, significant correlations between the P4HA1 mRNA level and the mRNA level of several EMT and stem cell markers were found. Conclusions:A high P4HA1 mRNA level, as a single-gene surrogate of hypoxia, is an independent prognostic marker for the overall survival and locoregional recurrence of OSCC patients.

SUBMITTER: Kappler M

PROVIDER: S-EPMC5833914 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background Early-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease. Methods and findings Tumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy. Conclusions Our findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.

Project description:BackgroundThe prognosis of oral squamous cell carcinoma (OSCC) patients is difficult to predict or describe due to its high-level heterogeneity and complex aetiologic factors. Ferroptosis is a novel form of iron-dependent cell death that is closely related to tumour growth and progression. This study aims to clarify the predictive value of ferroptosis-related genes (FRGs) on the overall survival(OS) of OSCC patients.MethodsThe mRNA expression profile of FRGs and clinical information of patients with OSCC were collected from the TCGA database. Candidate differentially expressed ferroptosis-related genes (DE-FRGs) were identified by analysing differences between OSCC and adjacent normal tissues. A gene signature of prognosis-related DE-FRGs was established by univariate Cox analysis and LASSO analysis in the training set. Patients were then divided into high- and low-risk groups according to the cut-off value of risk scores, A nomogram was constructed to quantify the contributions of gene signature and clinical parameters to OS. Then several bioinformatics analyses were used to verify the reliability and accuracy of the model in the validation set. Finally, single-sample gene set enrichment analysis (ssGSEA) was also performed to reveal the underlying differences in immune status between different risk groups.ResultsA prognostic model was constructed based on 10 ferroptosis-related genes. Patients in high-risk group had a significantly worse OS (p < 0.001). The gene signature was verified as an independent predictor for the OS of OSCC patients (HR > 1, p < 0.001). The receiver operating characteristic curve displayed the favour predictive performance of the risk model. The prediction nomogram successfully quantified each indicator's contribution to survival and the concordance index and calibration plots showed its superior predictive capacity. Finally, ssGSEA preliminarily indicated that the poor prognosis in the high-risk group might result from the dysregulation of immune status.ConclusionThis study established a 10-ferroptosis-releated gene signature and nomogram that can be used to predict the prognosis of OSCC patients, which provides new insight for future anticancer therapies based on potential FRG targets.

Dataset Information

P4HA1: A single-gene surrogate of hypoxia signatures in oral squamous cell carcinoma patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets