HGSOC remains the most common type of ovarian cancer with the highest incidence and the strongest fatality rate all over the world, and there is no definite research conclusion on its tumorigenesis mechanism. Meanwhile, due to the lack of effective early screening methods, most patients with high-grade serous ovarian cancer are at the advanced stage of tumor progression at the time of diagnosis, accompanied by extensive peritoneal metastasis, while most patients will experience tumor recurrence, both of the above two factors together lead to a very poor prognosis for patients (2, 5). However, the 5-year survival rate of patients with early stages of HGSOC is as high as 92%, which is 62% higher than that with later stages of HGSOC (25), which suggests the possibility that patients with HGSOC can benefit from efficient early screening methods. Many researchers have conducted extensive research in this field, and have also discovered some novel diagnostic markers with clinical application value (9–11). But the objects of these studies are all at the transcriptome level, and protein, as a more direct manifestation of the life activities of cells, organs and even the body, may be a better research carrier for the screening of high-efficiency diagnostic or prognostic biomarkers. Furthermore, with the accumulation of proteomic of data in public databases, it also provides more feasibility for this research idea.
WGCNA is a powerful method based on "guilt-by-association", and algorithms based on WGCNA transform the complexity of gene expression data sets into advantages, which can clarify gene relationships above the noise level (16). At present, WGCNA has been widely used in the field of tumor research, greatly promoting the biological interpretation of high-throughput sequencing data from clear cell renal cell carcinoma, pancreatic ductal adenocarcinoma (PDAC) as well as ovarian cancer, and assisting the discovery of tumor diagnosis and prognostic biomarkers (26–28). However, the number of studies applying WGCNA to analysis proteomic data is relatively small for the time being, but gratifying results have been achieved. Mantini et al. suggested that three proteins SPTBN1, KHSRP and PYGL might serve as potential prognostic biomarker for PDAC (29). While Johnson et al. identified proteins and biological processes in Alzheimer’s disease brain that might serve as therapeutic targets and fluid biomarkers for the disease (30). These results also illustrate the effectiveness of WGCNA applied to proteomic data.
In this study, we obtained proteomic data of HGSOC samples from the CPTAC database. Then these data were assessed by WGCNA, and the brown module significantly related to HGSOC was identified. Interestingly, the brown module is not only significantly related to HGSOC, but also significantly related to the patient's survival time and significantly negatively related to the clinical stage and histological grade of HGSOC. The results of enrichment analysis of proteins in brown module show that most of these proteins are related to the organization and function of the extracellular matrix (ECM) components including collagen (COL1A1, COL1A2, COL4A1, COL4A2, COL6A1, COL6A2 etc.), proteoglycan (LUM, DCN), laminin (LAMA4, LAMB1, LAMB2, LAMC1) and other proteins as linkers to connect the above proteins (NID1, PRELP, TNXB), covering almost all types of ECM components, and most of these proteins were down-regulated in our data and were consistent with the results of previous studies (2). The metastasis-prone characteristics of HGSOC play an important role in its relatively poor prognosis (6, 7). Tumor invasion and metastasis is a complicated pathological process which involving interactions between tumor cells and various biologically active molecules from tumor microenvironment including ECM (31), and for malignant tumor cells derived from epithelial cells like HGSOC, epithelial-mesenchymal transition (EMT) is the key first biological step for these tumors cells to metastasize, which is accompanied by disorders of ECM composition and organization, and in turn enhancing tumor cell mobility and protecting tumor cells from immune attack via collagen remodeling, and finally promoting the invasion and metastasis of HGSOC (5, 32, 33). These facts not only prove the credibility of the correlation between the brown module and the clinical stage, histological grade and survival time of patients, but also indirectly prove the validity of our analysis results.
Moreover, the PPI analysis was conducted to screen hub genes, which were further performed with survival analysis using our data and verified by a transcriptome dataset of HGSOC from the TCGA and GTEx database due to the lack of other available proteomic data. Finally, only ALB showed a good consistence between expression pattern and correlation with the patient's prognosis, that was, the expression level of ALB in tumor tissues was significantly down-regulated and had a significantly positive correlation with the patient's prognosis. Therefore, we speculate that ALB may be involved in the prognosis of HGSOC patients. ALB encodes the secreted and main protein of human blood, lymph, cerebrospinal and interstitial fluid, which plays important roles in a variety of physiological functions, such as maintaining colloid osmotic pressure and acting as the carrier protein for a wide range of endogenous molecules including hormones, fatty acids, as well as exogenous drugs (34). ALB has been reported to participate in the development and treatment of tumors with different mechanisms. First, previous studies have shown that the production of ALB is significantly inhibited during cancer-related systemic inflammation, which is regulated by a variety of cytokines and growth factors produced by tumor cells and immune cells (35). Secondly, the decrease in plasma ALB concentration reflects the poor nutritional condition of cancer patients, which may be related to the chemotherapy resistance of patients (36). Further, ALB might take part in antioxidant and anticancer effect (37, 38). In addition, the low serum ALB concentration is related to the poor survival time of patients with various cancers (35), as well as for ovarian cancer (39). However, these studies have not clarified the definite mechanism of how low plasma ALB levels lead to poor prognosis of patients. Meanwhile, there is no report about the relationship between ALB and the prognosis of HGSOC, and our research fills this gap. Moreover, the samples in our study are from tumor tissues of HGSOC patients, which may provide a certain research basis for the subsequent understanding of the role of ALB in the occurrence of HGSOC.
The limitation of this study is that the verification of our results was carried out in the transcriptome data due to the lack of other independent proteomic data. Secondly, due to the limitation of mass spectrometry technology, the relative abundance of most proteins is missing, and in order to ensure the reliability of the results, these proteins are not included into our analysis, which may cause bias to our conclusion.