A 4-gene signature predicts prognosis of uterine serous carcinoma

Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling

BACKGROUND

Although uterine serous carcinoma (USC) represents a small proportion of all uterine cancer cases, patients with this aggressive subtype typically have high rates of chemotherapy resistance and disease recurrence that collectively result in a disproportionately high death rate. The goal of this study was to provide a deeper view of the tumor microenvironment of this poorly characterized uterine cancer variant through multi-region microsampling and quantitative proteomics.

METHODS

Tumor epithelium, tumor-involved stroma, and whole "bulk" tissue were harvested by laser microdissection (LMD) from spatially resolved levels from nine USC patient tumor specimens and underwent proteomic analysis by mass spectrometry and reverse phase protein arrays, as well as transcriptomic analysis by RNA-sequencing for one patient's tumor.

RESULTS

LMD enriched cell subpopulations demonstrated varying degrees of relatedness, indicating substantial intratumor heterogeneity emphasizing the necessity for enrichment of cellular subpopulations prior to molecular analysis. Known prognostic biomarkers were quantified with stable levels in both LMD enriched tumor and stroma, which were shown to be highly variable in bulk tissue. These USC data were further used in a comparative analysis with a data generated from another serous gynecologic malignancy, high grade serous ovarian carcinoma, and have been added to our publicly available data analysis tool, the Heterogeneity Analysis Portal ( https://lmdomics.org/ ).

CONCLUSIONS

Here we identified extensive three-dimensional heterogeneity within the USC tumor microenvironment, with disease-relevant biomarkers present in both the tumor and the stroma. These data underscore the critical need for upfront enrichment of cellular subpopulations from tissue specimens for spatial proteogenomic analysis.

Uterine serous carcinoma: assessing association between genomics and patterns of metastasis

Background

Uterine serous carcinoma (USC) is an aggressive subtype of endometrial carcinoma which has been increasing at alarming rates, particularly among Asian, Hispanic and Black women. USC has not been well characterized in terms of mutational status, pattern of metastases and survival.

Objective

To investigate the association between sites of recurrence and metastases of USC, mutational status, race, and overall survival (OS).

Methods

This single-center retrospective study evaluated patients with biopsy-proven USC that underwent genomic testing between January 2015 and July 2021. Association between genomic profile and sites of metastases or recurrence was performed using χ2 or Fisher's exact test. Survival curves for ethnicity and race, mutations, sites of metastasis/recurrence were estimated using the Kaplan-Meier method and compared with log-rank test. Cox proportional hazard regression models were used to examine the association between OS with age, race, ethnicity, mutational status, and sites of metastasis/recurrence. Statistical analyses were performed using SAS Software Version 9.4.

Results

The study included 67 women (mean age 65.8 years, range 44-82) with 52 non-Hispanic women (78%) and 33 Black women (49%). The most common mutation was TP53 (55/58 women, 95%). The peritoneum was the most common site of metastasis (29/33, 88%) and recurrence (8/27, 30%). PR expression was more common in women with nodal metastases (p=0.02) and non-Hispanic women (p=0.01). ERBB2 alterations were more common in women with vaginal cuff recurrence (p=0.02), while PIK3CA mutation was more common in women with liver metastases (p=0.048). ARID1A mutation and presence of recurrence or metastases to the liver were associated with lower OS (Hazard Ratio (HR): 31.87; 95%CI: 3.21, 316.9; p<0.001 and HR: 5.66; 95%CI: 1.2, 26.79; p=0.01, respectively). In the bivariable Cox model, the presence of metastasis/recurrence to the liver and/or the peritoneum were both independent significant predictors of OS (HR: 9.8; 95%CI: 1.85-52.7; p=0.007 and HR: 2.7; 95%CI: 1.02-7.1; p=0.04, respectively).

Conclusions

TP53 is often mutated in USC, which most commonly metastasize and recur in the peritoneum. OS was shorter in women with ARID1A mutations and with metastasis/recurrence to the liver. The presence of metastasis/recurrence to liver and/or peritoneum were independently associated with shorter OS.

CXCR3 predicts the prognosis of endometrial adenocarcinoma

OBJECTIVES

Currently, endometrial adenocarcinoma lacks an effective prognostic indicator. This study was to develop and validate a gene biomarker and a nomogram to predict the survival of endometrial adenocarcinoma, explore potential mechanisms and select sensitive drugs.

METHODS

425 endometrial adenocarcinoma cases with RNA sequencing data from TCGA were used to identify the most immune-related module by WGCNA. As an external test set, 103 cases from GSE17025 were used. Immune-related genes were downloaded from Innate DB. The three sets of data were used to identify the prognostic genes. Based on 397 cases with complete clinical data from TCGA, randomly divided into the training set (n = 199) and test set (n = 198), we identified CXCR3 as the prognostic gene biomarker. Age, grade, FIGO stage, and risk were used to develop and validate a predictive nomogram. AUC, C-index, calibration curve and K-M estimate evaluated the model's predictive performance. KEGG enrichment analysis, immune functions, TMB, the effectiveness of immunotherapy, and drug sensitivity between the high-risk and low-risk groups.

RESULTS

CXCR3 was identified as a prognostic biomarker. We calculated the risk score and divided the cases into the high-risk and low-risk groups by the median value of the risk score. The OS of the high-risk group was better than the low-risk group. The risk was the prognostic indicator independent of age, grade, and FIGO stage. We constructed the nomogram including age, grade, FIGO stage, and risk to predict the prognosis of endometrial adenocarcinoma. The top five KEGG pathways enriched by the DEGs between the high- and low-risk groups were viral protein interaction with cytokine and cytokine receptors, cytokine-cytokine receptor interaction, chemokine signaling pathway, natural killer cell-mediated cytotoxicity, and cell adhesion molecules. We analyzed the difference in immune cells and found that CD8+ T cells, activated CD4+ T cells, T helper cells, monocytes, and M1 macrophages were infiltrated more in the low-risk group. However, M0 macrophages and activated dendritic cells were more in the high-risk group. The immune function including APC coinhibition, APC costimulation, CCR, checkpoint, cytolytic activity, HLA, inflammation-promoting, MHC-I, parainflammation, T cell coinhibition, T cell costimulation, type I-IFN-response, and type II-IFN-response were better in the low-risk group. TMB and TIDE scores were both better in the low-risk group. By 'the pRRophetic' package, we found 56 sensitive drugs for different risk groups.

CONCLUSION

We identified CXCR3 as the prognostic biomarker. We also developed and validated a predictive nomogram model combining CXCR3, age, histological grade, and FIGO stage for endometrial adenocarcinoma, which could help explore the precise treatment.

Nine-gene signature and nomogram for predicting survival in patients with head and neck squamous cell carcinoma

Background: Head and neck squamous cell carcinomas (HNSCCs) are derived from the mucosal linings of the upper aerodigestive tract, salivary glands, thyroid, oropharynx, larynx, and hypopharynx. The present study aimed to identify the novel genes and pathways underlying HNSCC. Despite the advances in HNSCC research, diagnosis, and treatment, its incidence continues to rise, and the mortality of advanced HNSCC is expected to increase by 50%. Therefore, there is an urgent need for effective biomarkers to predict HNSCC patients’ prognosis and provide guidance to the personalized treatment.

Methods: Both HNSCC clinical and gene expression data were abstracted from The Cancer Genome Atlas (TCGA) database. Intersecting analysis was adopted between the gene expression matrix of HNSCC patients from TCGA database to extract TME-related genes. Differential gene expression analysis between HNSCC tissue samples and normal tissue samples was performed by R software. Then, HNSCC patients were categorized into clusters 1 and 2 via NMF. Next, TME-related prognosis genes (p &lt; 0.05) were analyzed by univariate Cox regression analysis, LASSO Cox regression analysis, and multivariate Cox regression analysis. Finally, nine genes were selected to construct a prognostic risk model and a prognostic gene signature. We also established a nomogram using relevant clinical parameters and a risk score. The Kaplan–Meier curve, survival analysis, time-dependent receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and the concordance index (C-index) were carried out to assess the accuracy of the prognostic risk model and nomogram. Potential molecular mechanisms were revealed by gene set enrichment analysis (GSEA). Additionally, gene correlation analysis and immune cell correlation analysis were conducted for further enriching our results.

Results: A novel HNSCC prognostic model was established based on the nine genes (GTSE1, LRRN4CL, CRYAB, SHOX2, ASNS, KRT23, ANGPT2, HOXA9, and CARD11). The value of area under the ROC curves (AUCs) (0.769, 0.841, and 0.816) in TCGA whole set showed that the model effectively predicted the 1-, 3-, and 5-year overall survival (OS). Results of the Cox regression assessment confirmed the nine-gene signature as a reliable independent prognostic factor in HNSCC patients. The prognostic nomogram developed using multivariate Cox regression analysis showed a superior C-index over other clinical signatures. Also, the calibration curve had a high level of concordance between estimated OS and the observed OS. This showed that its clinical net can precisely estimate the one-, three-, and five-year OS in HNSCC patients. The gene set enrichment analysis (GSEA) to some extent revealed the immune- and tumor-linked cascades.

Conclusion: In conclusion, the TME-related nine-gene signature and nomogram can effectively improve the estimation of prognosis in patients with HNSCC.

SOX9: Advances in Gynecological Malignancies

Transcription factors of the SOX family were first discovered in mammals in 1990. The sex-determining region Y box 9 belongs to the SOX transcription factor family. It plays an important role in inducing tissue and cell morphogenesis, survival, and many developmental processes. Furthermore, it has been shown to be an oncogene in many tumors. Gynecological malignancies are tumors that occur in the female reproductive system and seriously threaten the lives of patients. Common gynecological malignancies include ovarian cancer, cervical cancer, and endometrial cancer. So far, the molecular mechanisms related to the incidence and development of gynecological malignancies remain unclear. This makes it particularly important to discover their common causative molecule and thus provide an effective therapeutic target. In recent years, studies have found that multiple mechanisms are involved in regulating the expression of the sex-determining region Y box 9, leading to the occurrence and development of gynecological malignancies. In this review, we discuss the prognostic value of SOX9 expression and the potential of targeting SOX9 for gynecological malignancy treatment. We also discuss progress regarding the role of SOX9 in gynecological malignancy pathogenesis through its mediation of important mechanisms, including tumor initiation and proliferation, apoptosis, migration, invasion, chemoresistance, and stem cell maintenance.