The coagulome of oral squamous cell carcinoma: examining the role and regulation of coagulation in oral cancers using a systems approach

Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links

It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing strategies for the effective therapy of cancer, as it is an important factor that determines the evolution and treatment response of tumours. This work combines machine learning and single-cell RNA sequencing (scRNA-seq) to explore the glioma tumour microenvironment's TME. With the help of genome-wide association studies (GWAS) and Mendelian randomization (MR), we found genetic variants associated with TME elements that affect cancer and cardiovascular disease outcomes. Using machine learning techniques high dimensional data was analysed to obtain new molecular sub-types and biomarkers that are important for prognosis and treatment response. F3 was identified as a top regulator and revealed potential angiogenic and immunogenic characteristics within the TME that could be harnessed in immunotherapy. These results demonstrate the potential of machine-learning approaches in identifying and dissecting TME heterogeneity and informing treatment in precision oncology. This work proposes improving the immunotherapeutic response through targeted modulation of relevant cellular and molecular interactions.

Prediction of Oral Cancer Biomarkers by Salivary Proteomics Data

Oral cancer, representing 2-4% of all cancer cases, predominantly consists of Oral Squamous Cell Carcinoma (OSCC), which makes up 90% of oral malignancies. Early detection of OSCC is crucial, and identifying specific proteins in saliva as biomarkers could greatly improve early diagnosis. Here, we proposed a strategy to pinpoint candidate biomarkers. Starting from a list of salivary proteins detected in 10 OSCC patients and 20 healthy controls, we combined a univariate approach and a multivariate approach to select candidates. To reduce the number of proteins selected, a Protein-Protein Interaction network was built to consider only connected proteins. Then, an over-representation analysis (ORA) determined the enriched pathways. The network from 172 differentially abundant proteins highlighted 50 physically connected proteins, selecting relevant candidates for targeted experimental validations. Notably, proteins like Heat shock 70 kDa protein 1A/1B, Pyruvate kinase PKM, and Phosphoglycerate kinase 1 were suggested to be differentially regulated in OSCC patients, with implications for oral carcinogenesis and tumor growth. Additionally, the ORA revealed enrichment in immune system, complement, and coagulation pathways, all known to play roles in tumorigenesis and cancer progression. The employed method has successfully identified potential biomarkers for early diagnosis of OSCC using an accessible body fluid.

Causal relationships between risk of venous thromboembolism and 18 cancers: a bidirectional Mendelian randomization analysis

BACKGROUND

People with cancer experience high rates of venous thromboembolism (VTE). Risk of subsequent cancer is also increased in people experiencing their first VTE. The causal mechanisms underlying this association are not completely understood, and it is unknown whether VTE is itself a risk factor for cancer.

METHODS

We used data from large genome-wide association study meta-analyses to perform bidirectional Mendelian randomization analyses to estimate causal associations between genetic liability to VTE and risk of 18 different cancers.

RESULTS

We found no conclusive evidence that genetic liability to VTE was causally associated with an increased incidence of cancer, or vice versa. We observed an association between liability to VTE and pancreatic cancer risk [odds ratio for pancreatic cancer: 1.23 (95% confidence interval: 1.08-1.40) per log-odds increase in VTE risk, P = 0.002]. However, sensitivity analyses revealed this association was predominantly driven by a variant proxying non-O blood group, with inadequate evidence to suggest a causal relationship.

CONCLUSIONS

These findings do not support the hypothesis that genetic liability to VTE is a cause of cancer. Existing observational epidemiological associations between VTE and cancer are therefore more likely to be driven by pathophysiological changes which occur in the setting of active cancer and anti-cancer treatments. Further work is required to explore and synthesize evidence for these mechanisms.