Effect of VTCN1 on progression and metastasis of ovarian carcinoma in vitro and vivo

Unlocking molecular mechanisms and identifying druggable targets in matched-paired brain metastasis of breast and lung cancers

Introduction

The incidence of brain metastases in cancer patients is increasing, with lung and breast cancer being the most common sources. Despite advancements in targeted therapies, the prognosis remains poor, highlighting the importance to investigate the underlying mechanisms in brain metastases. The aim of this study was to investigate the differences in the molecular mechanisms involved in brain metastasis of breast and lung cancers. In addition, we aimed to identify cancer lineage-specific druggable targets in the brain metastasis.

Methods

To that aim, a cohort of 44 FFPE tissue samples, including 22 breast cancer and 22 lung adenocarcinoma (LUAD) and their matched-paired brain metastases were collected. Targeted gene expression profiles of primary tumors were compared to their matched-paired brain metastases samples using nCounter PanCancer IO 360™ Panel of NanoString technologies. Pathway analysis was performed using gene set analysis (GSA) and gene set enrichment analysis (GSEA). The validation was performed by using Immunohistochemistry (IHC) to confirm the expression of immune checkpoint inhibitors.

Results

Our results revealed the significant upregulation of cancer-related genes in primary tumors compared to their matched-paired brain metastases (adj. p ≤ 0.05). We found that upregulated differentially expressed genes in breast cancer brain metastasis (BM-BC) and brain metastasis from lung adenocarcinoma (BM-LUAD) were associated with the metabolic stress pathway, particularly related to the glycolysis. Additionally, we found that the upregulated genes in BM-BC and BM-LUAD played roles in immune response regulation, tumor growth, and proliferation. Importantly, we identified high expression of the immune checkpoint VTCN1 in BM-BC, and VISTA, IDO1, NT5E, and HDAC3 in BM-LUAD. Validation using immunohistochemistry further supported these findings.

Conclusion

In conclusion, the findings highlight the significance of using matched-paired samples to identify cancer lineage-specific therapies that may improve brain metastasis patients outcomes.

KRT19 is a Promising Prognostic Biomarker and Associates with Immune Infiltrates in Serous Ovarian Cystadenocarcinoma

Background

Ovarian cancer (OV) is the highest prevalent gynecologic tumor with complicated pathogenesis; high-grade serous ovarian cystadenocarcinoma (HGSOC) is the most epidemiological and malignant subtype of OV. Keratin type I cytoskeleton 19 (KRT19) is an intermediate filament protein which plays essential roles in the maintenance of epithelial cells. However, its role in OV remains largely unknown.

Methods

Bioinformatic analysis with various databases was conducted in this study. In details, KRT19 expression was assessed using databases including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Omnibus (GEO) and Human Protein Atlas (HPA). GO-KEGG and GSEA analysis were performed by R packages. The biological function of KRT19 was analyzed based on the single-cell sequencing information from CancerSEA database. The association of KRT19 expression with immunomodulator and chemokine was predicted via the TISIDB database.

Results

The expression of KRT19 was significantly upregulated in ovarian samples compared with normal controls. KRT19 expression was negatively associated with prognosis in OV, and further analysis revealed that KRT19 had promising diagnostic significance in distinguishing OV cancer from normal samples. GO-KEGG and GSEA analysis indicated that KRT19 was associated with multiple biological functions and pathways including epidermis development, apical junction, inflammatory response, and epithelial mesenchymal transition. By using different GEO series, we found that KRT19 was differentially expressed in OV-associated tissues. Furthermore, the increased KRT19 expression was positively correlated with the immune infiltration levels of the most immune cells in OV.

Conclusion

This study demonstrated that KRT19 is a promising prognosis and diagnosis biomarker that determines cancer progression and is correlated with tumor immune cells infiltration in OV, suggesting being a molecular target for immunotherapies.

Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer

Simple Summary

Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. The aim of our study was to broadly measure protein biomarkers to find tests for the early detection of ovarian cancer. We found that combinations of 4–7 protein biomarkers can provide highly accurate detection of early- and late-stage ovarian cancer compared to benign conditions. The performance of the tests was then validated in a second independent cohort.

Abstract

Background: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. Methods: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). Results: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. Conclusions: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.

Overexpression of Epithelial Splicing Regulatory Protein 1 in Metastatic Lesions of Serous Ovarian Carcinoma Correlates with Poor Patient Prognosis

Background: Epithelial splicing regulatory proteins (ESRPs) can regulate alternative splicing of RNA and play roles in tumorigenesis and development of various malignancies. In this study, bioinformatic analyses and immunohistochemistry (IHC) were used to investigate the function of ESPRs in serous ovarian carcinoma (SOC) oncogenesis and metastasis. Materials and Methods: The mRNA levels of ESRPs were analyzed by Oncomine and gene expression profiling interactive analysis (GEPIA). Prognostic values of ESRPs were analyzed by GEPIA and the UALCAN website. Genetic variations of ESRPs were analyzed by cBioPortal. ESRP1 was selected for further research. The relationship between ESRP1 and immunoregulatory molecules was studied by using the TISIDB database. ESRP1 protein expression in OC was investigated via IHC assays. Results: ESRP1 and ESRP2 mRNA were significantly upregulated in SOC (p < 0.05). The prognostic value of ESRP1 mRNA in SOC was inconsistent, and ESRP2 mRNA level did not relate to prognosis for OC patients. The IHC results showed higher ESRP1 expression in OC tissues than in normal ovarian tissues (p = 0.002), and ESRP1 expression in metastatic lesions of OC patients was higher than in paired primary OC tissues (p = 0.035). The ESRP1 expression was related to FIGO stage, differentiation, and peritoneal metastasis (p = 0.016; 0.031; 0.038, respectively). The ESRP1 switch (the differential expression of ESRP1 between metastatic and primary tumor of ovarian carcinoma) was significantly associated with E-cadherin expression in metastatic OC tumors (p = 0.012). The ESRP1 expression in both metastasis and ESRP1 switch significantly correlated with poor prognosis of OC patients (p = 0.045; 0.038, respectively), and ESRP1 switch and FIGO stage were independent risk factors for OC patient prognosis (p = 0.033; 0.009, respectively). Conclusions: The ESRP1 may promote OC metastasis by promoting OC cell colonization via the mesenchymal-epithelial transition (MET) process. The ESRP1 expression in metastatic lesions of OC patients may be a biomarker for predicting prognosis and a potential therapeutic target in OC.

Clinical Research on the Mechanisms Underlying Immune Checkpoints and Tumor Metastasis

This study highlights aspects of the latest clinical research conducted on the relationship between immune checkpoints and tumor metastasis. The overview of each immune checkpoint is divided into the following three sections: 1) structure and expression; 2) immune mechanism related to tumor metastasis; and 3) clinical research related to tumor metastasis. This review expands on the immunological mechanisms of 17 immune checkpoints, including TIM-3, CD47, and OX-40L, that mediate tumor metastasis; evidence shows that most of these immune checkpoints are expressed on the surface of T cells, which mainly exert immunomodulatory effects. Additionally, we have summarized the roles of these immune checkpoints in the diagnosis and treatment of metastatic tumors, as these checkpoints are considered common predictors of metastasis in various cancers such as prostate cancer, non-Hodgkin lymphoma, and melanoma. Moreover, certain immune checkpoints can be used in synergy with PD-1 and CTLA-4, along with the implementation of combination therapies such as LIGHT-VTR and anti-PD-1 antibodies. Presently, most monoclonal antibodies generated against immune checkpoints are under investigation as part of ongoing preclinical or clinical trials conducted to evaluate their efficacy and safety to establish a better combination treatment strategy; however, no significant progress has been made regarding monoclonal antibody targeting of CD28, VISTA, or VTCN1. The application of immune checkpoint inhibitors in early stage tumors to prevent tumor metastasis warrants further evidence; the immune-related adverse events should be considered before combination therapy. This review aims to elucidate the mechanisms of immune checkpoint and the clinical progress on their use in metastatic tumors reported over the last 5 years, which may provide insights into the development of novel therapeutic strategies that will assist with the utilization of various immune checkpoint inhibitors.

B7-H4, a promising target for immunotherapy

The coinhibitory molecule B7-H4, an important member of the B7 family, is abnormally expressed in tumors, inflammation and autoimmune diseases. B7-H4 negatively regulates T cell immune response and promotes immune escape by inhibiting the proliferation, cytokine secretion, and cell cycle of T cells. Moreover, B7-H4 plays an extremely important role in tumorigenesis and tumor development including cell proliferation, invasion, metastasis, anti-apoptosis, etc. In addition, B7-H4 has the other biological functions, such as protection against type 1 diabetes (T1D) and islet cell transplantation. Therefore, B7-H4 has been identified as a novel marker or a therapeutic target for the treatment of tumors, inflammation, autoimmune diseases, and organ transplantation. Here, we summarized the expression profiles, physiological and pathological functions, and regulatory mechanisms of B7-H4, the signaling pathways involved, as well as B7-H4-based immunotherapy.

Quantifying critical states of complex diseases using single-sample dynamic network biomarkers

Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DNB. In this study, we developed a novel method, i.e., single-sample DNB (sDNB), to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient, thus opening a new way to predict diseases in a personalized way. In contrast to the information of differential expressions used in traditional biomarkers to “diagnose disease”, sDNB is based on the information of differential associations, thereby having the ability to “predict disease” or “diagnose near-future disease”. Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples. We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer, thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level.

The concept of dynamic network biomarkers (DNB) was proposed for detecting the critical state or tipping point of a complex disease (a pre-disease state immediately preceding the disease state), and has been applied to study the mechanism of cell fate decision and immune checkpoint blockade. But DNB cannot be used to identify the critical state or tipping point for a single patient because evaluating DNB for critical state required the data of multiple samples. The proposed method can identify the critical state of a complex disease for a single patient by implementing the concept of DNB. This method not only can be applied to detect the critical state or tipping point of a single sample, but also can be used to study the mechanism of complex disease at a single sample level. The ability of accurately and efficiently identifying the critical state for a single sample can benefit the development of personalized medicine.

Identification of genes differentially expressed in menstrual breakdown and repair

STUDY QUESTION

Does the changing molecular profile of the endometrium during menstruation correlate with the histological profile of menstruation.

SUMMARY ANSWER

We identified several genes not previously associated with menstruation; on Day 2 of menstruation (early-menstruation), processes related to inflammation are predominantly up-regulated and on Day 4 (late-menstruation), the endometrium is predominantly repairing and regenerating.

WHAT IS KNOWN ALREADY

Menstruation is induced by progesterone withdrawal at the end of the menstrual cycle and involves endometrial tissue breakdown, regeneration and repair. Perturbations in the regulation of menstruation may result in menstrual disorders including abnormal uterine bleeding.

STUDY DESIGN, SIZE DURATION

Endometrial samples were collected by Pipelle biopsy on Days 2 (n = 9), 3 (n = 9) or 4 (n = 6) of menstruation.

PARTICIPANTS/MATERIALS, SETTING, METHODS

RNA was extracted from endometrial biopsies and analysed by genome wide expression Illumina Sentrix Human HT12 arrays. Data were analysed using 'Remove Unwanted Variation-inverse (RUV-inv)'. Ingenuity pathway analysis (IPA) and the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 were used to identify canonical pathways, upstream regulators and functional gene clusters enriched between Days 2, 3 and 4 of menstruation. Selected individual genes were validated by quantitative PCR.

MAIN RESULTS AND THE ROLE OF CHANCE

Overall, 1753 genes were differentially expressed in one or more comparisons. Significant canonical pathways, gene clusters and upstream regulators enriched during menstrual bleeding included those associated with immune cell trafficking, inflammation, cell cycle regulation, extracellular remodelling and the complement and coagulation cascade. We provide the first evidence for a role for glutathione-mediated detoxification (glutathione-S-transferase mu 1 and 2; GSTM1 and GSTM2) during menstruation. The largest number of differentially expressed genes was between Days 2 and 4 of menstruation (n = 1176). We identified several genes not previously associated with menstruation including lipopolysaccharide binding protein, serpin peptidase inhibitor, clade B (ovalbumin), member 3 (SERPINB3) and -4 (SERPINB4), interleukin-17C (IL17C), V-set domain containing T-cell activation inhibitor 1 (VTCN1), proliferating cell nuclear antigen factor (KIAA0101/PAF), trefoil factor 3 (TFF3), laminin alpha 2 (LAMA2) and serine peptidase inhibitor, Kazal type 1 (SPINK1). Genes related to inflammatory processes were up-regulated on Day 2 (early-menstruation), and those associated with endometrial repair and regeneration were up-regulated on Day 4 (late-menstruation).

LIMITATIONS, REASONS FOR CAUTION

Participants presented with a variety of endometrial pathologies related to bleeding status and other menstrual characteristics. These variations may also have influenced the menstrual process.

WIDER IMPLICATIONS OF THE FINDINGS

The temporal molecular profile of menstruation presented in this study identifies a number of genes not previously associated with the menstrual process. Our findings provide valuable insight into the menstrual process and may present novel targets for therapeutic intervention in cases of endometrial dysfunction.

LARGE SCALE DATA

All microarray data have been deposited in the public data repository Gene Expression Omnibus (GSE86003).

STUDY FUNDING AND COMPETING INTERESTS

Funding for this work was provided by a National Health and Medical Research Council of Australia (NHMRC) Project Grant APP1008553 to M.H., P.R. and J.G. M.H. is supported by an NHMRC Practitioner Fellowship. P.P. is supported by a NHMRC Early Career Fellowship. The authors have no conflict of interest to declare.