Quiescin Sulfhydryl Oxidase 2 Overexpression Predicts Poor Prognosis and Tumor Progression in Patients With Colorectal Cancer: A Study Based on Data Mining and Clinical Verification

The synchronous upregulation of a specific protein cluster in the blood predicts both colorectal cancer risk and patient immune status

BACKGROUND

Early detection and treatment of colorectal cancer (CRC) is crucial for improving patient survival rates. This study aims to identify signature molecules associated with CRC, which can serve as valuable indicators for clinical hematological screening.

METHOD

We have systematically searched the Human Protein Atlas database and the relevant literature for blood protein-coding genes. The CRC dataset from TCGA was used to compare the acquired genes and identify differentially expressed molecules (DEMs). Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify modules of co-expressed molecules and key molecules within the DEMs. Signature molecules of CRC were then identified from the key molecules using machine learning. These findings were further validated in clinical samples. Finally, Logistic regression was used to create a predictive model that calculated the likelihood of CRC in both healthy individuals and CRC patients. We evaluated the model's sensitivity and specificity using the ROC curve.

RESULT

By utilizing the CRC dataset, WGCNA analysis, and machine learning, we successfully identified seven signature molecules associated with CRC from 1478 blood protein-coding genes. These markers include S100A11, INHBA, QSOX2, MET, TGFBI, VEGFA and CD44. Analyzing the CRC dataset showed its potential to effectively discriminate between CRC and normal individuals. The up-regulated expression of these markers suggests the existence of an immune evasion mechanism in CRC patients and is strongly correlated with poor prognosis.

CONCLUSION

The combined detection of the seven signature molecules in CRC can significantly enhance diagnostic efficiency and serve as a novel index for hematological screening of CRC.

Site-specific immobilization of the endosialidase reveals QSOX2 is a novel polysialylated protein

Polysialic acid (polySia) is a linear polymer of α2,8-linked sialic acid residues that is of fundamental biological interest due to its pivotal roles in the regulation of the nervous, immune, and reproductive systems in healthy human adults. PolySia is also dysregulated in several chronic diseases, including cancers and mental health disorders. However, the mechanisms underpinning polySia biology in health and disease remain largely unknown. The polySia-specific hydrolase, endoneuraminidase NF (EndoN), and the catalytically inactive polySia lectin EndoNDM, have been extensively used for studying polySia. However, EndoN is heat stable and remains associated with cells after washing. When studying polySia in systems with multiple polysialylated species, the residual EndoN that cannot be removed confounds data interpretation. We developed a strategy for site-specific immobilization of EndoN on streptavidin-coated magnetic beads. We showed that immobilizing EndoN allows for effective removal of the enzyme from samples, while retaining hydrolase activity. We used the same strategy to immobilize the polySia lectin EndoNDM, which enabled the enrichment of polysialylated proteins from complex mixtures such as serum for their identification via mass spectrometry. We used this methodology to identify a novel polysialylated protein, QSOX2, which is secreted from the breast cancer cell line MCF-7. This method of site-specific immobilization can be utilized for other enzymes and lectins to yield insight into glycobiology.

QSOX2 Upregulated in triple-negative breast cancer exacerbates patient prognosis by stabilizing integrin β1

Breast cancer (BC) remains a significant global health threat, with triple-negative breast cancer (TNBC) standing out as a particularly aggressive subtype lacking targeted therapies. Addressing this gap, we propose Quiescin Q6 sulfhydryl oxidase 2 (QSOX2) as a potential therapeutic target, a disulfide bond-forming enzyme implicated in cancer progression. Using publicly available datasets, we conducted a comprehensive analysis of QSOX2 expression in BC tumor and non-tumor tissues, assessing its specificity across different molecular subtypes. We further explored correlations between QSOX2 expression and patient outcomes, utilizing datasets like TCGA and METABRIC. In addition, we performed in vitro experiments to evaluate QSOX2 expression in BC cell lines and investigate the effects of QSOX2 knockdown on various TNBC cellular processes, including cell proliferation, apoptosis resistance, migration, and the epithelial-to-mesenchymal transition (EMT). Our results reveal significantly elevated QSOX2 expression in BC tumor tissues, particularly in TNBC, and establish an association between high QSOX2 expression and increased patient mortality, cancer progression, and recurrence across various BC subtypes. Notably, QSOX2 knockdown in TNBC cell lines reduces cell proliferation, enhances apoptosis, and suppresses migration, potentially mediated through its influence on the EMT process. Furthermore, we identify a significant link between QSOX2 and integrin β1 (ITGB1), suggesting that QSOX2 enhances ITGB1 stability, subsequently exacerbating the malignancy of TNBC. In conclusion, elevated QSOX2 expression emerges as a key factor associated with adverse patient outcomes in BC, particularly in TNBC, contributing to disease progression through various mechanisms, including the modulation of ITGB1 stability. Our findings underscore the potential of targeting QSOX2 as a therapeutic strategy for improving patient prognoses not only in TNBC but also in other BC subtypes.

ALDH3A2, ODF2, QSOX2, and MicroRNA-503-5p Expression to Forecast Recurrence in TMPRSS2-ERG-Positive Prostate Cancer

Following radical surgery, patients may suffer a relapse. It is important to identify such patients so that therapy tactics can be modified appropriately. Existing stratification schemes do not display the probability of recurrence with enough precision since locally advanced prostate cancer (PCa) is classified as high-risk but is not ranked in greater detail. Between 40 and 50% of PCa cases belong to the TMPRSS2-ERG subtype that is a sufficiently homogeneous group for high-precision prognostic marker search to be possible. This study includes two independent cohorts and is based on high throughput sequencing and qPCR data. As a result, we have been able to suggest a perspective-trained model involving a deep neural network based on both qPCR data for mRNA and miRNA and clinicopathological criteria that can be used for recurrence risk forecasts in patients with TMPRSS2-ERG-positive, locally advanced PCa (the model uses ALDH3A2 + ODF2 + QSOX2 + hsa-miR-503-5p + ISUP + pT, with an AUC = 0.944). In addition to the prognostic model’s use of identified differentially expressed genes and miRNAs, miRNA–target pairs were found that correlate with the prognosis and can be presented as an interactome network.