Dataset Information

Prognostic biomarkers of cervical squamous cell carcinoma identified via plasma metabolomics.

ABSTRACT: Cervical cancer is the second most common female malignancy worldwide. The metabolic profile of plasma associated with the prognosis of cervical cancer remains poorly understood. In this cross-sectional study, plasma samples were collected from three groups of patients with CSCC, namely primary patients before treatment (BT group), patients with a poor prognosis (PP group, including patients with distant metastasis and local recurrence), and patients with a good prognosis within two years after the first treatment (GP group). The plasma metabolomics was conducted to detect the dynamic changes of metabolites via ultra-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Multivariate analyses, including principle component, partial least square-discriminant, and orthogonal projection to latent structure-discriminant analyses, were performed to compare each pair of the three groups. The differential metabolites were identified by comparison of the exact m/z values and mass spectrometry (MS)/MS spectra with the structural information of the metabolites obtained from the Human Metabolome Database (http://www.hmdb.ca/) and LIPID MAPS (http://www.lipidmaps.org/). To screen for potential markers, receiver operating characteristic curve analysis of the differential metabolites. Finally, thirty plasma samples were collected from each group. Multivariate analyses showed that 31 metabolites were significantly different among the 3 groups studied. Of those, the 5 metabolites phosphatidyl choline (15:0/16:0), phosphatidyl glycerol (12:0/13:0), actosylceramide (d18:1/16:0), D-Maltose, and phthalic acid, with an area under the curve above 0.75, were identified as potential biomarkers. The present findings provide evidence for biomarkers to monitor prognosis of patients with CSCC, which may help in better managing the disease.

SUBMITTER: Zhou H

PROVIDER: S-EPMC6617423 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prognostic biomarkers of cervical squamous cell carcinoma identified via plasma metabolomics.

Zhou Huihui H Li Qi Q Wang Tong T Liang Hong H Wang Yanan Y Duan Yani Y Song Min M Wang Yaoxian Y Jin Hong H

Medicine 20190601 26

Cervical cancer is the second most common female malignancy worldwide. The metabolic profile of plasma associated with the prognosis of cervical cancer remains poorly understood. In this cross-sectional study, plasma samples were collected from three groups of patients with CSCC, namely primary patients before treatment (BT group), patients with a poor prognosis (PP group, including patients with distant metastasis and local recurrence), and patients with a good prognosis within two years after ...[more]

PMID: 31261558

Similar Datasets

Project description:BACKGROUND:Cervical cancer (CC) represents the fourth most frequently diagnosed malignancy affecting women all over the world. However, effective prognostic biomarkers are still limited for accurately identifying high-risk patients. Here, we provided a combination machine learning algorithm-based signature to predict the prognosis of cervical squamous cell carcinoma (CSCC). METHODS AND MATERIALS:After utilizing RNA sequencing (RNA-seq) data from 36 formalin-fixed and paraffin-embedded (FFPE) samples, the most significant modules were highlighted by the weighted gene co-expression network analysis (WGCNA). A candidate genes-based prognostic classifier was constructed by the least absolute shrinkage and selection operator (LASSO) and then validated in an independent validation set. Finally, based on the multivariate analysis, a nomogram including the FIGO stage, therapy outcome, and risk score level was built to predict progression-free survival (PFS) probability. RESULTS:A mRNA-based signature was developed to classify patients into high- and low-risk groups with significantly different PFS and overall survival (OS) rate (training set: p?<?0.001 for PFS, p?=?0.016 for OS; validation set: p?=?0.002 for PFS, p?=?0.028 for OS). The prognostic classifier was an independent and powerful prognostic biomarker for PFS in both cohorts (training set: hazard ratio [HR]?=?0.13, 95% CI 0.05-0.33, p?<?0.001; validation set: HR?=?0.02, 95% CI 0.01-0.04, p?<?0.001). A nomogram that integrated the independent prognostic factors was constructed for clinical application. The calibration curve showed that the nomogram was able to predict 1-, 3-, and 5-year PFS accurately, and it performed well in the external validation cohorts (concordance index: 0.828 and 0.864, respectively). CONCLUSION:The mRNA-based biomarker is a powerful and independent prognostic factor. Furthermore, the nomogram comprising our prognostic classifier is a promising predictor in identifying the progression risk of CSCC patients.

Project description:BackgroundOral squamous cell carcinoma (OSCC) is the most malignant neoplasm in oral cancer. There is growing evidence that its progression involves altered metabolism. The current method of evaluating prognosis is very limited, and metabolomics may provide a new approach for quantitative evaluation. The aim of the study is to evaluate the use of metabolomics as prognostic markers for patients with OSCC.MethodsAn analytical platform, Ultra-Performance Liquid Chromatography-Quadrupole/Orbitrap High Resolution Mass Spectrometry (UHPLC-Q-Orbitrap HRMS), was used to acquire the serum fingerprinting profiles from a total of 103 patients of OSCC before and after the operation. In total, 103 OSCC patients were assigned to either a training set (n = 73) or a test set (n = 30). The potential biomarkers and the changes of serum metabolites were profiled and correlated with the clinicopathological parameters and survival of the patients by statistical analysis. To further verify our results, we linked them to gene expression using data from the Kyoto Encyclopedia of Genes and Genomes (KEGG).ResultsIn total, 14 differential metabolites and five disturbed pathways were identified between the preoperative group and postoperative group. Succinic acid change-low, hypoxanthine change-high tumor grade, and tumor stage indicated a trend towards improved recurrence-free survival (RFS), whether in a training set or a test set. In addition, succinic acid change-low, hypoxanthine change-high, and tumor grade provided the highest predictive accuracy of the patients with OSCC. KEGG enrichment analysis showed that the imbalance in the amino acid and purine metabolic pathway may affect the prognosis of OSCC.ConclusionsThe changes of metabolites before and after operation may be related to the prognosis of OSCC patients. UHPLC-Q-Orbitrap HRMS serum metabolomics analysis could be used to further stratify the prognosis of patients with OSCC. These results can better understand the mechanisms related to early recurrence and help develop more effective therapeutic targets.

Project description:The immune response within the tumor microenvironment plays a key role in tumorigenesis and determines the clinical outcomes of head and neck squamous cell carcinoma (HNSCC). However, to date, a paucity of robust, reliable immune-related biomarkers has been identified that are capable of estimating prognosis in HNSCC patients. High-throughput RNA sequencing was performed in tumors and matched adjacent tissues from five HNSCC patients, and the immune signatures expression of 730 immune-related transcripts selected from the nCounter PanCancer Immune Profiling Panel were assessed. Survival analyzes were performed in a training cohort, consisting of 416 HNSCC cases, retrieved from The Cancer Genome Atlas (TCGA) database. A prognostic signature was built, using elastic net-penalized Cox regression and backward, stepwise Cox regression analyzes. The outcomes were validated by an independent cohort of 115 HNSCC patients, using tissue microarrays and immunohistochemistry staining. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was also used to estimate the relative fractions of 22 immune-cell types and their correlations coefficients with prognostic biomarkers. Collectively, 248 immune-related genes were differentially expressed in paired tumors and normal tissues using RNA sequencing. After process screening in the training TCGA cohort, four immune-related genes (PVR, TNFRSF12A, IL21R, and SOCS1) were significantly associated with overall survival (OS). Integrating these genes with Path_N stage, a multiplex model was built and suggested better performance in determining 5 years OS (receiver operating characteristic (ROC) analysis, area under the curve (AUC)=0.709) than others. Further protein-based validation was conducted in 115 HNSCC patients. Similarly, high expression of PVR and TNFRSF12A were associated with poor OS (Kaplan-Meier p=0.017 and 0.0032), while high expression of IL21R and SOCS1 indicated favorable OS (Kaplan-Meier p<0.0001 and =0.0018). The integrated model with Path_N stage still demonstrated efficacy in OS evaluation (Kaplan-Meier p<0.0001, ROC AUC=0.893). Besides, the four prognostic genes were significantly correlated with activated CD8+ T cells, CD4+ T cells, follicular helper T cells and regulatory T cells, implying the possible involvement of these genes in the immunoregulation and development of HNSCC. The well-established model encompassing both immune-related biomarkers and clinicopathological factor might serve as a promising tool for the prognostic prediction of HNSCC.

Dataset Information

Prognostic biomarkers of cervical squamous cell carcinoma identified via plasma metabolomics.

Publications

Prognostic biomarkers of cervical squamous cell carcinoma identified via plasma metabolomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets