Dataset Information

Molecular Differences between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers.

ABSTRACT: The most frequently diagnosed histological types of cervical cancer (CC) are squamous cell carcinoma (SCC) and adenocarcinoma (ADC). Clinically, the prognosis of both types is controversial. A molecular profile that distinguishes each histological subtype and predicts the prognosis would be of great benefit to CC patients.

Methods

The transcriptome of CC patients from The Cancer Genome Atlas (TCGA) was analyzed using the DESeq2 package to obtain the differentially expressed genes (DEGs) between ADC and SCC. The DEGs were validated on a publicly available Mexican-Mestizo patient transcriptome dataset (GSE56303). The global biological pathways involving the DEGs were obtained using the Webgestalt platform. The associations of the DEGs with Overall Survival (OS) were assessed. Finally, three DEGs were validated by RT-qPCR in an independent cohort of Mexican patients.

Results

The molecular profiles of ADC and SCC of the CC patients of the TCGA database and the Mexican-Mestizo cohort (GSE56303) were determined obtaining 1768 and 88 DEGs, respectively. Strikingly, 70 genes were concordant-with similar Log2FoldChange values-in both cohorts. The 70 DEGs were involved in IL-17, JAK/STAT, and Ras signaling. Kaplan-Meier OS analysis from the Mexican-Mestizo cohort showed that higher GABRB2 and TSPAN8 and lower TMEM40 expression were associated with better OS. Similar results were found in an independent Mexican cohort.

Conclusions

Molecular differences were detected between the ADC and SCC subtypes; however, further studies are required to define the appropriate prognostic biomarker for each histological type.

SUBMITTER: Campos-Parra AD

PROVIDER: S-EPMC9322365 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.