Adenosquamous Carcinoma of the Uterine Cervix - Impact of Histology on Clinical Management

Gene Expression Analysis for Uterine Cervix and Corpus Cancer Characterization

The analysis of gene expression quantification data is a powerful and widely used approach in cancer research. This work provides new insights into the transcriptomic changes that occur in healthy uterine tissue compared to those in cancerous tissues and explores the differences associated with uterine cancer localizations and histological subtypes. To achieve this, RNA-Seq data from the TCGA database were preprocessed and analyzed using the KnowSeq package. Firstly, a kNN model was applied to classify uterine cervix cancer, uterine corpus cancer, and healthy uterine samples. Through variable selection, a three-gene signature was identified (VWCE, CLDN15, ADCYAP1R1), achieving consistent 100% test accuracy across 20 repetitions of a 5-fold cross-validation. A supplementary similar analysis using miRNA-Seq data from the same samples identified an optimal two-gene miRNA-coding signature potentially regulating the three-gene signature previously mentioned, which attained optimal classification performance with an 82% F1-macro score. Subsequently, a kNN model was implemented for the classification of cervical cancer samples into their two main histological subtypes (adenocarcinoma and squamous cell carcinoma). A uni-gene signature (ICA1L) was identified, achieving 100% test accuracy through 20 repetitions of a 5-fold cross-validation and externally validated through the CGCI program. Finally, an examination of six cervical adenosquamous carcinoma (mixed) samples revealed a pattern where the gene expression value in the mixed class aligned closer to the histological subtype with lower expression, prompting a reconsideration of the diagnosis for these mixed samples. In summary, this study provides valuable insights into the molecular mechanisms of uterine cervix and corpus cancers. The newly identified gene signatures demonstrate robust predictive capabilities, guiding future research in cancer diagnosis and treatment methodologies.

Immune landscape and heterogeneity of cervical squamous cell carcinoma and adenocarcinoma

Despite the differences in disease outcomes and pathological features between cervical squamous cell carcinoma (CSCC) and adenocarcinoma (ADC), the molecular characteristics in immune heterogeneity of the tumor microenvironment remain unclear. Here, we explored the immune landscape and heterogeneity between CSCC and ADC. Gene expression and clinical characteristics of cervical carcinoma from The Cancer Genome Atlas (TCGA) were downloaded. Differentially expressed genes (DEGs), immune cell infiltration, and pathway enrichment analyses were used to explore the immune landscape and heterogeneity between CSCC and ADC. Furthermore, distinct immune signatures between CSCC and ADC were validated based on clinical samples. In total, 4,132 upregulated DEGs and 2,307 down-regulated DEGs were identified between CSCC and ADC, with enrichments in immune related-pathways in CSCC. In addition, 54 hub DEGs correlated with patients' prognosis and immunocytes infiltration were identified. The CSCC patients had a higher ImmuneScore and more abundant immunocytes infiltration compared to ADC patients, as validated by immunohistochemistry (IHC) and multicolor immunofluorescence (mIF) analyses of collected samples. Furthermore, CSCC displayed higher inhibitory immune checkpoints expression, tumor mutation burden (TMB), and microsatellite instability (MSI) compared to ADC, which indicated CSCC patients were more likely to benefit from immunotherapy. In summary, our results revealed the huge immune heterogeneity between CSCC and ADC, and provided guidance for immunotherapy selection for different pathological types of cervical cancer.

Incidence and risk factors for the development of cerebral metastasis in cervical cancer patients

OBJECTIVE

Cerebral metastasis (CM) in cervical cancer (CC) cases, although rare, results in high lethality rates. The present study aimed to assess CM incidence in a Brazilian reference CC center and evaluate the risk factors for CM development. Retrospective observational study of patients diagnosed with CC between 2010 and 2017.

METHODS

Cumulative CM incidence and incidence density were evaluated. Characteristics associated to CM development risks were identified using crude (cOR) or adjusted (aOR) odds ratios.

RESULTS

A total of 3,397 patients were included in this study. Patient age ranged from 18 to 101 years, with a mean age of 48.8±14.0. After a mean follow-up time of 3.2±2.1 years, 51 CM cases were identified, resulting in a cumulative incidence of 1.5% (95% confidence intervals [CI]=1.12-1.97) and an incidence density at the end of the 6th year of 27.4 per 1,000 women/year. Advanced clinical stage (aOR=3.15; 95% CI=1.16-8.58; p=0.025), the presence of previous lung metastasis (aOR=4.04; 95% CI=1.82-8.94; p=0.001) and the adenocarcinoma (aOR=2.90; 95% CI=1.46-5.76; p=0.002), adenosquamous carcinoma (aOR=7.33; 95% CI=2.87-18.73; p<0.001), undifferentiated carcinoma (aOR=14.37; 95% CI=3.77-54.76; p<0.001) and neuroendocrine carcinoma (aOR=21.31; 95% CI=6.65-68.37, p<0.001) histological types were associated with a higher risk for CM development. CM risk was higher in the first years of follow-up, with no cases observed after the 6th year.

CONCLUSION

CC patients in advanced clinical stages, displaying previous lung metastasis and non-squamous histological types are at high risk of developing CM.