Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers

BBOX1 mediates metabolic reprogramming driven by hypoxia and participates in the malignant progress of high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is the most aggressive type of ovarian cancer that causes great threats to women's health. Therefore, we performed RNA-sequencing technology in clinical samples to explore the molecular mechanisms underlying the progression of HGSOC. We then noticed BBOX1, a kind of 2-oxoglutarate-dependent enzyme that is highly expressed in HGSOC tumor tissues. Functional studies showed that BBOX1 promotes cell survival and growth of HGSOC cells in vitro and in vivo. Overexpression of the wild-type BBOX1 promoted cell proliferation, but the Asn191 and Asn292 mutation (key amino acid for the enzymatic activity of BBOX1) counteracted this effect (P < 0.05), which indicated that the promotion effect of BBOX1 on HGSOC cell proliferation was related to its catalytic activity. Downregulation of BBOX1 reduced the activity of the mTORC1 pathway, and decreased protein expression of IP3R3 and phosphorylation level of S6KThr389. Metabolomics analysis revealed that BBOX1 is implicated in the glucose metabolism, amino acid metabolism, and nucleotide metabolism of HGSOC cells. In addition, inhibition of BBOX1 suppressed HGSOC cell glycolysis and decreased glucose consumption, lactate production, and the expression of key factors in glycolysis. Finally, we found hypoxia induced the expression of BBOX1 in HGSOC cells and confirmed that BBOX1 could be transcriptionally activated by hypoxia-inducible factor-1α, which could directly bind to the BBOX1 promoter. In summary, BBOX1 mediated the metabolic reprogramming driven by hypoxia, and affected cell metabolism through the mTORC1 pathway, thus acting as an oncogene during HGSOC development.

Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches

Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.

AUTOSURV: INTERPRETABLE DEEP LEARNING FRAMEWORK FOR CANCER SURVIVAL ANALYSIS INCORPORATING CLINICAL AND MULTI-OMICS DATA

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can reveal the underlying disease mechanisms at the molecular level. In this study, we developed a novel deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian cancer patients. Our model achieved significantly better prognosis prediction than the conventional Cox Proportional Hazard model and other competitive deep learning approaches in various settings. Moreover, an interpretation approach was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that made important contributions to distinguishing predicted high- and low-risk patients. The identified associations were partially supported by previous studies.

Artificial intelligence assists precision medicine in cancer treatment

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.