Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Prognosis prediction and drug guidance of ovarian serous cystadenocarcinoma through mitochondria gene-based model

BACKGROUND

Mitochondrial dysregulation contributes to the chemoresistance of multiple cancer types. Yet, the functions of mitochondrial dysregulation in Ovarian serous cystadenocarcinoma (OSC) remain largely unknown.

AIM

We sought to investigate the function of mitochondrial dysregulation in OSC from the bioinformatics perspective. We aimed to establish a model for prognosis prediction and chemosensitivity evaluation of the OSC patients by targeting mitochondrial dysregulation.

METHODS

Differentially expressed genes (DEGs) were screened from the Cancer Genome Atlas (TCGA)-OV dataset and the mitochondrial-related DEGs were identified from the Human MitoCarta 3.0 database. Prognosis-related mitochondria-related genes (MRGs) were screened to establish the MRGs-based risk score model for prognosis prediction. To validate the risk score model, the risk score model was then evaluated by IHC staining intensity and survival curves from clinical specimens of OSC patients. Migration and proliferation assays were performed to elucidate the role of carcinogenic gene ACSS3 in serous ovarian cancer cell lines.

RESULTS

Using consensus clustering algorithm, we identified 341 MRGs and two subtypes of OSC patients. Moreover, we established a novel prognostic risk score model by combining the transcription level, intensity and extent scores of MRGs for prognosis prediction purpose. The model was established using 7 MRGs (ACOT13, ACSS3, COA6, HINT2, MRPL14, NDUFC2, and NDUFV2) significantly correlated to the prognosis of OSC. Importantly, by performing the drug sensitivity analysis, we found that the OSC patients in the low-risk group were more sensitive to cisplatin, paclitaxel and docetaxel than those in the high-risk group, while the latter ones were more sensitive to VEGFR inhibitor Axitinib and BRAF inhibitors Vemurafenib and SB590885. In addition, patients in the low-risk group were predicted to have better response in anti-PD-1 immunotherapy than those in the high-risk group. The risk score model was then validated by survival curves of high-risk and low-risk groups determined by IHC staining scores of OSC clinical samples. The carcinogenic effect of ACSS3 in OSC was confirmed through the knockdown of ACSS3 in SKOV3 and HO-8910 cells.

CONCLUSION

To summarize, we established a novel 7 MRGs - based risk score model that could be utilized for prognosis prediction and chemosensitivity assessment in OSC patients.

Simple and robust high-throughput serum proteomics workflow with low-microflow LC-MS/MS

Clinical proteomics has substantially advanced in identifying and quantifying proteins from biofluids, such as blood, contributing to the discovery of biomarkers. The throughput and reproducibility of serum proteomics for large-scale clinical sample analyses require improvements. High-throughput analysis typically relies on automated equipment, which can be costly and has limited accessibility. In this study, we present a rapid, high-throughput workflow low-microflow LC-MS/MS method without automation. This workflow was optimized to minimize the preparation time and costs by omitting the depletion and desalting steps. The developed method was applied to data-independent acquisition (DIA) analysis of 235 samples, and it consistently yielded approximately 6000 peptides and 600 protein groups, including 33 FDA-approved biomarkers. Our results demonstrate that an 18-min DIA high-throughput workflow, assessed through intermittently collected quality control samples, ensures reproducibility and stability even with 2 µL of serum. It was successfully used to analyze serum samples from patients with diabetes having chronic kidney disease (CKD), and could identify five dysregulated proteins across various CKD stages.

De novo missense variants in HDAC3 leading to epigenetic machinery dysfunction are associated with a variable neurodevelopmental disorder

Histone deacetylase 3 (HDAC3) is a crucial epigenetic modulator essential for various developmental and physiological functions. Although its dysfunction is increasingly recognized in abnormal phenotypes, to our knowledge, there have been no established reports of human diseases directly linked to HDAC3 dysfunction. Using trio exome sequencing and extensive phenotypic analysis, we correlated heterozygous de novo variants in HDAC3 with a neurodevelopmental disorder having variable clinical presentations, frequently associated with intellectual disability, developmental delay, epilepsy, and musculoskeletal abnormalities. In a cohort of six individuals, we identified missense variants in HDAC3 (c.277G>A [p.Asp93Asn], c.328G>A [p.Ala110Thr], c.601C>T [p.Pro201Ser], c. 797T>C [p.Leu266Ser], c.799G>A [p.Gly267Ser], and c.1075C>T [p.Arg359Cys]), all located in evolutionarily conserved sites and confirmed as de novo. Experimental studies identified defective deacetylation activity in the p.Asp93Asn, p.Pro201Ser, p.Leu266Ser, and p.Gly267Ser variants, positioned near the enzymatic pocket. In addition, proteomic analysis employing co-immunoprecipitation revealed that the disrupted interactions with molecules involved in the CoREST and NCoR complexes, particularly in the p.Ala110Thr variant, consist of a central pathogenic mechanism. Moreover, immunofluorescence analysis showed diminished nuclear to cytoplasmic fluorescence ratio in the p.Ala110Thr, p.Gly267Ser, and p.Arg359Cys variants, indicating impaired nuclear localization. Taken together, our study highlights that de novo missense variants in HDAC3 are associated with a broad spectrum of neurodevelopmental disorders, which emphasizes the complex role of HDAC3 in histone deacetylase activity, multi-protein complex interactions, and nuclear localization for proper physiological functions. These insights open new avenues for understanding the molecular mechanisms of HDAC3-related disorders and may inform future therapeutic strategies.

Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning

Background

Ovarian cancer (OC) is the most lethal gynecological cancer in the United States. Among the different types of OC, serous ovarian cancer (SOC) stands out as the most prevalent. Transcriptomics techniques generate extensive gene expression data, yet only a few of these genes are relevant to clinical diagnosis.

Methods

Methods for feature selection (FS) address the challenges of high dimensionality in extensive datasets. This study proposes a computational framework that applies FS techniques to identify genes highly associated with platinum-based chemotherapy response on SOC patients. Using SOC datasets from the Gene Expression Omnibus (GEO) database, LASSO and varSelRF FS methods were employed. Machine learning classification algorithms such as random forest (RF) and support vector machine (SVM) were also used to evaluate the performance of the models.

Results

The proposed framework has identified biomarkers panels with 9 and 10 genes that are highly correlated with platinum-paclitaxel and platinum-only response in SOC patients, respectively. The predictive models have been trained using the identified gene signatures and accuracy of above 90% was achieved.

Conclusions

In this study, we propose that applying multiple feature selection methods not only effectively reduces the number of identified biomarkers, enhancing their biological relevance, but also corroborates the efficacy of drug response prediction models in cancer treatment.

CD47-a novel prognostic predicator in epithelial ovarian cancer and correlations with clinicopathological and gene mutation features

BACKGROUND

Epithelial ovarian cancer (EOC) is insensitive to immunotherapy due to its poor immunogenicity; thus, suitable biomarkers need to be identified for better prognostic stratification and individualized treatment. CD47 is a novel immunotherapy target; however, its impact on EOC prognosis is controversial and correlation with genetic features is unclear. The aim of this study was to investigate the prognostic significance of CD47 and its correlations with biological behaviors and genetic features of EOC.

METHODS

Immunohistochemistry (IHC) and next-generation sequencing (NGS) were performed to examine expressions of CD47, PD-L1, and genomic mutations in the tissue samples of 75 EOC patients. Various clinicopathologic and genomic features were then evaluated to determine their correlation with CD47 expression. Kaplan-Meier analysis and Cox regression analysis were used to identify independent prognostic factors. Risk score modeling was then established, and the predictive capacity of this model was further confirmed by nomogram analysis.

RESULTS

CD47 was mainly expressed in the tumor cell membrane and cytoplasm, and the rate of high CD47 expression was 63.7%. CD47 expression was associated with various clinicopathological factors, including FIGO stage, CA125 and HE4 value, presence of multidisciplinary surgeries, presence and volume of ascites, lymph-node metastasis, Ki-67 index and platinum-resistant, as well as genetic characteristics like BRCA mutation, HRD status, and TP53 mutation in EOC. Patients with high CD47 expression showed worse prognosis than the low-expression group. Cox regression analysis demonstrated that CA125, CD47, and BRCA mutation were independent factors for EOC prognosis. Patients were then categorized into high-risk and low-risk subgroups based on the risk score of the aforementioned independent factors, and the prognosis of the high-risk group was worse than those of the low-risk group. The nomogram showed adequate discrimination with a concordance index of 0.777 (95% CI, 0.732-0.822). The calibration curve showed good consistency.

CONCLUSION

CD47 correlated with various malignant biology and genetic characteristics of EOC and may play pivotal and multifaceted roles in the tumor microenvironment of EOC Finally, we constructed a reliable prediction model centered on CD47 and integrated CA125 and BRCA to better guide high-risk population management.

Purification and characterization of different proteasome species from mammalian cells

Proteasomes are heterogeneous in forms and functions, but how the equilibrium among the 20S, 26S, and 30S proteasomes is achieved and altered is elusive. Here, we present a protocol for purifying and characterizing proteasome species. We describe steps for generating stable cell lines; affinity purifying the proteasome species; and characterizing them through native PAGE, activity assay, size-exclusion chromatography, and mass spectrometry. These standardized methods may contribute to biochemical studies of cellular proteasomes under both physiological and pathological conditions. For complete details on the use and execution of this protocol, please refer to Choi et al. (2023).1.

The Role of NQO1 in Ovarian Cancer

Ovarian cancer is one of the most dangerous gynecologic malignancies showing a high fatality rate because of late diagnosis and relapse occurrence due to chemoresistance onset. Several researchers reported that oxidative stress plays a key role in ovarian cancer occurrence, growth and development. The NAD(P)H:quinone oxidoreductase 1 (NQO1) is an antioxidant enzyme that, using NADH or NADPH as substrates to reduce quinones to hydroquinones, avoids the formation of the highly reactive semiquinones, then protecting cells against oxidative stress. In this review, we report evidence from the literature describing the effect of NQO1 on ovarian cancer onset and progression.

Label-Free Quantification Mass Spectrometry Identifies Protein Markers of Chemotherapy Response in High-Grade Serous Ovarian Cancer

Eighty percent of ovarian cancer patients initially respond to chemotherapy, but the majority eventually experience a relapse and die from the disease with acquired chemoresistance. In addition, 20% of patients do not respond to treatment at all, as their disease is intrinsically chemotherapy resistant. Data-independent acquisition nano-flow liquid chromatography-mass spectrometry (DIA LC-MS) identified the three protein markers: gelsolin (GSN), calmodulin (CALM1), and thioredoxin (TXN), to be elevated in high-grade serous ovarian cancer (HGSOC) tissues from patients that responded to chemotherapy compared to those who did not; the differential expression of the three protein markers was confirmed by immunohistochemistry. Analysis of the online GENT2 database showed that mRNA levels of GSN, CALM1, and TXN were decreased in HGSOC compared to fallopian tube epithelium. Elevated levels of GSN and TXN mRNA expression correlated with increased overall and progression-free survival, respectively, in a Kaplan-Meier analysis of a large online repository of HGSOC patient data. Importantly, differential expression of the three protein markers was further confirmed when comparing parental OVCAR-5 cells to carboplatin-resistant OVCAR-5 cells using DIA LC-MS analysis. Our findings suggest that GSN, CALM1, and TXN may be useful biomarkers for predicting chemotherapy response and understanding the mechanisms of chemotherapy resistance. Proteomic data are available via ProteomeXchange with identifier PXD033785.