High Serpin Family A Member 10 Expression Confers Platinum Sensitivity and Is Associated With Survival Benefit in High-Grade Serous Ovarian Cancer: Based on Quantitative Proteomic Analysis

Correlation analysis of the impact of Clonorchis sinensis juvenile on gut microbiota and transcriptome in mice

Clonorchiasis remains a non-negligible global zoonosis, imposing serious socio-economic burdens in endemic regions. The interplay between gut microbiota and the host transcriptome is crucial for maintaining health; however, the impact of Clonorchiasis sinensis juvenile infection on these factors is still poorly understood. This study aimed to investigate their relationship and potential pathogenic mechanisms. The BALB/c mouse model of early infection with C. sinensis juvenile was constructed. Pathological analyses revealed that C. sinensis juvenile triggered liver inflammation, promoted intestinal villi growth, and augmented goblet cell numbers in the ileum. Additionally, the infection altered the diversity and structure of gut microbiota, particularly affecting beneficial bacteria that produce short-chain fatty acids, such as Lactobacillus and Muribaculaceae, and disrupted the Firmicutes/Bacteroidetes ratio. Gut transcriptome analysis demonstrated an increase in the number of differentially expressed genes (DEGs) as infection progressed. Enriched Gene Ontology items highlighted immune and detoxification-related processes, including immunoglobulin production and xenobiotic metabolic processes. Kyoto Encyclopedia of Genes and Genomes pathway analysis further indicated involvement in circadian rhythm, as well as various detoxification and metabolic-related pathways (e.g., glutathione metabolism and glycolysis/gluconeogenesis). Prominent DEGs associated with these pathways included Igkv12-41, Mcpt2, Arntl, Npas2, Cry1, and Gsta1. Correlation analysis additionally identified Bacteroides_sartorii as a potential key regulator in the interaction between gut microbiota and transcriptome. This study sheds light on the alterations in gut microbiota and transcriptome in mice following C. sinensis juvenile infection, as well as their correlation, laying a foundation for a better understanding of their interaction during infection.

IMPORTANCE

This study highlighted the impact of C. sinensis juvenile infection on the gut microbiota and transcriptome of BALB/c mice. It induced liver inflammation, promoted intestinal villi growth, and altered goblet cell numbers. The infection also disrupted the diversity and structure of gut microbiota, particularly affecting beneficial bacteria. Transcriptome analysis revealed increased expression of genes related to immune response and detoxification processes. Important pathways affected included circadian rhythm, glutathione metabolism, and glycolysis/gluconeogenesis. Notable genes implicated included Igkv12-41, Mcpt2, Arntl, Npas2, Cry1, and Gsta1. Bacteroides_sartorii emerged as a potential key regulator in this interaction.

Development and Validation of a Predictive Model for Resistance to Platinum-Based Chemotherapy in Patients with Ovarian Cancer through Proteomic Analysis

Platinum resistance in ovarian cancer poses a significant challenge, substantially impacting patient outcomes. Developing an accurate predictive model is crucial for improving clinical decision-making and guiding treatment strategies. Proteomic data from 217 high-grade serous ovarian cancer (HGSOC) biospecimens obtained from JHU, PNNL, and PTRC were used to construct a prediction model for identifying individuals who are resistant to platinum-based chemotherapy. A total of 6437 common proteins were detected across all data sets, with 26 proteins overlapping between the development cohorts JHU and PNNL. Using LASSO and logistic regression analysis, a six-protein model (P31323_PRKAR2B, Q13309_SKP2, Q14997_PSME4, Q6ZRP7_QSOX2, Q7LGA3_HS2ST1, and Q7Z2Z2_EFL1) was developed, which accurately predicted platinum resistance, with an AUC of 0.964 (95% CI, 0.929-0.999). Internal validation by resampling resulted in a C-index of 0.972 (95% CI 0.894-0.988). External validation performed on the PTRC cohort achieved an AUC of 0.855 (95% CI 0.748-0.963). Calibration curves showed good consistency, and DCA indicated superior clinical utility. The model also performed well in predicting PFS and OS at various time points. Based on these proteins, our predictive model can precisely predict platinum response and survival outcomes in HGSOC patients, which can assist clinicians in promptly identifying potentially platinum-resistant individuals.

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.

Clinical significance and immune infiltration analyses of a novel coagulation-related signature in ovarian cancer

Ovarian cancer (OV) is the most lethal gynecological malignancies worldwide. The coagulation cascade could induce tumor cell infiltration and contribute to OV progression. However, coagulation-related gene (CRG) signature for OV prognosis hasn't been determined yet. In this study, we evaluated the prognostic value of coagulation scores through receiver operating characteristics (ROC) analysis and K-M curves, among OV patients at our institution. Based on the transcriptome data of TCGA-OV cohort, we stratified two coagulation-related subtypes with distinct differences in prognosis and tumor immune microenvironment (p < 0.05). Moreover, from the 6406 differentially-expressed genes (DEGs) between the GTEx (n = 180) and TCGA-OV cohorts (n = 376), we identified 138 potential CRGs. Through LASSO-Cox algorithm, we finally distinguished a 3-gene signature (SERPINA10, CD38, and ZBTB16), with promising prognostic ability in both TCGA (p < 0.001) and ICGC cohorts (p = 0.040). Stepwise, we constructed a nomogram based on the clinical features and coagulation-related signature for overall survival prediction, with the C-index of 0.6761, which was evaluated by calibration curves. Especially, based on tissue microarrays analysis, Quantitative real-time fluorescence PCR (qRT-PCR), and Western Blot, we found that aberrant upregulation of CRGs was related to poor prognosis in OV at both mRNA and protein level (p < 0.05). Collectively, the coagulation-related signature was a robust prognostic biomarker, which could provide therapeutic benefits for chemotherapy/immunotherapy and assist clinical decision in OV patients.

Exploiting a living biobank to delineate mechanisms underlying disease-specific chromosome instability

Chromosome instability (CIN) is a cancer hallmark that drives tumour heterogeneity, phenotypic adaptation, drug resistance and poor prognosis. High-grade serous ovarian cancer (HGSOC), one of the most chromosomally unstable tumour types, has a 5-year survival rate of only ~30% - largely due to late diagnosis and rapid development of drug resistance, e.g., via CIN-driven ABCB1 translocations. However, CIN is also a cell cycle vulnerability that can be exploited to specifically target tumour cells, illustrated by the success of PARP inhibitors to target homologous recombination deficiency (HRD). However, a lack of appropriate models with ongoing CIN has been a barrier to fully exploiting disease-specific CIN mechanisms. This barrier is now being overcome with the development of patient-derived cell cultures and organoids. In this review, we describe our progress building a Living Biobank of over 120 patient-derived ovarian cancer models (OCMs), predominantly from HGSOC. OCMs are highly purified tumour fractions with extensive proliferative potential that can be analysed at early passage. OCMs have diverse karyotypes, display intra- and inter-patient heterogeneity and mitotic abnormality rates far higher than established cell lines. OCMs encompass a broad-spectrum of HGSOC hallmarks, including a range of p53 alterations and BRCA1/2 mutations, and display drug resistance mechanisms seen in the clinic, e.g., ABCB1 translocations and BRCA2 reversion. OCMs are amenable to functional analysis, drug-sensitivity profiling, and multi-omics, including single-cell next-generation sequencing, and thus represent a platform for delineating HGSOC-specific CIN mechanisms. In turn, our vision is that this understanding will inform the design of new therapeutic strategies.

Proteomic landscape of the extracellular matrix in the fibrotic kidney

The extracellular matrix (ECM) is a complex three-dimensional network of proteins surrounding cells, forming a niche that controls cell adhesion, proliferation, migration and differentiation. The ECM network provides an architectural scaffold for surrounding cells and undergoes dynamic changes in composition and contents during the evolution of chronic kidney disease (CKD). Here, we unveiled the proteomic landscape of the ECM by delineating proteome-wide and ECM-specific alterations in normal and fibrotic kidneys. Decellularized kidney tissue scaffolds were made and subjected to proteomic profiling by liquid chromatography with tandem mass spectrometry. A total of 172 differentially expressed proteins were identified in these scaffolds from mice with CKD. Through bioinformatics analysis and experimental validation, we identified a core set of nine signature proteins, which could play a role in establishing an oxidatively stressed, profibrotic, proinflammatory and antiangiogenetic microenvironment. Among these nine proteins, glutathione peroxidase 3 (GPX3) was the only protein with downregulated expression during CKD. Knockdown of GPX3 in vivo augmented ECM expression and aggravated kidney fibrotic lesions after obstructive injury. Transcriptomic profiling revealed that GPX3 depletion resulted in an altered expression of the genes enriched in hypoxia pathway. Knockdown of GPX3 induced NADPH oxidase 2 expression, promoted kidney generation of reactive oxygen species and activated p38 mitogen-activated protein kinase. Conversely, overexpression of exogenous GPX3 alleviated kidney fibrosis, inhibited NADPH oxidase 2 and p38 mitogen-activated protein kinase. These findings suggest that oxidative stress is a pivotal element of the fibrogenic microenvironment. Thus, our studies represent a comprehensive proteomic characterization of the ECM in the fibrotic kidney and provide novel insights into molecular composition of the fibrogenic microenvironment.

Extracellular Matrix in High-Grade Serous Ovarian Cancer: Advances in Understanding of Carcinogenesis and Cancer Biology

High-grade serous ovarian cancer (HGSOC) is notoriously known as the "silent killer" of post-menopausal women as it has an insidious progression and is the deadliest gynaecological cancer. Although a dual origin of HGSOC is now widely accepted, there is growing evidence that most cases of HGSOC originate from the fallopian tube epithelium. In this review, we will address the fallopian tube origin and involvement of the extracellular matrix (ECM) in HGSOC development. There is limited research on the role of ECM at the earliest stages of HGSOC carcinogenesis. Here we aim to synthesise current understanding on the contribution of ECM to each stage of HGSOC development and progression, beginning at serous tubal intraepithelial carcinoma (STIC) precursor lesions and proceeding across key events including dissemination of tumourigenic fallopian tube epithelial cells to the ovary, survival of these cells in peritoneal fluid as multicellular aggregates, and colonisation of the ovary. Likewise, as part of the metastatic series of events, serous ovarian cancer cells survive travel in peritoneal fluid, attach to, migrate across the mesothelium and invade into the sub-mesothelial matrix of secondary sites in the peritoneal cavity. Halting cancer at the pre-metastatic stage and finding ways to stop the dissemination of ovarian cancer cells from the primary site is critical for improving patient survival. The development of drug resistance also contributes to poor survival statistics in HGSOC. In this review, we provide an update on the involvement of the ECM in metastasis and drug resistance in HGSOC. Interplay between different cell-types, growth factor gradients as well as evolving ECM composition and organisation, creates microenvironment conditions that promote metastatic progression and drug resistance of ovarian cancer cells. By understanding ECM involvement in the carcinogenesis and chemoresistance of HGSOC, this may prompt ideas for further research for developing new early diagnostic tests and therapeutic strategies for HGSOC with the end goal of improving patient health outcomes.

Improved profiling of low molecular weight serum proteome for gastric carcinoma by data-independent acquisition

Low molecular weight proteins (LMWPs) in the bloodstream participate in various biological processes and are closely associated with disease status, whereas identification of serous LMWPs remains a great technical challenge due to the wide dynamic range of protein components. In this study, we constructed an integrated LMWP library by combining the LMWPs obtained by three enrichment methods (50% ACN, 20% ACN + 20 mM ABC, and 30 kDa) and their fractions identified by the data-dependent acquisition method. With this newly constructed library, we comprehensively profiled LMWPs in serum using data-independent acquisition and reliably achieved quantitative results for 75% serous LMWPs. When applying this strategy to quantify LMWPs in human serum samples, we could identify 405 proteins on average per sample, of which 136 proteins were with a MW less than 30 kDa and 293 proteins were with a MW less than 65 kDa. Of note, pre- and post-operative gastric carcinoma (GC) patients showed differentially expressed serous LWMPs, which was also different from the pattern of LWMP expression in healthy controls. In conclusion, our results showed that LMWPs could efficiently distinguish GC patients from healthy controls as well as between pre- and post-operative statuses, and more importantly, our newly developed LMWP profiling platform could be used to discover candidate LMWP biomarkers for disease diagnosis and status monitoring.

The Role of the Extracellular Matrix and Tumor-Infiltrating Immune Cells in the Prognostication of High-Grade Serous Ovarian Cancer

Ovarian cancer is the eighth global leading cause of cancer-related death among women. The most common form is the high-grade serous ovarian carcinoma (HGSOC). No further improvements in the 5-year overall survival have been seen over the last 40 years since the adoption of platinum- and taxane-based chemotherapy. Hence, a better understanding of the mechanisms governing this aggressive phenotype would help identify better therapeutic strategies. Recent research linked onset, progression, and response to treatment with dysregulated components of the tumor microenvironment (TME) in many types of cancer. In this study, using bioinformatic approaches, we identified a 19-gene TME-related HGSOC prognostic genetic panel (19 prognostic genes (PLXNB2, HMCN2, NDNF, NTN1, TGFBI, CHAD, CLEC5A, PLXNA1, CST9, LOXL4, MMP17, PI3, PRSS1, SERPINA10, TLL1, CBLN2, IL26, NRG4, and WNT9A) by assessing the RNA sequencing data of 342 tumors available in the TCGA database. Using machine learning, we found that specific patterns of infiltrating immune cells characterized each risk group. Furthermore, we demonstrated the predictive potential of our risk score across different platforms and its improved prognostic performance compared with other gene panels.