Gastric cancer (GC) is among the leading causes of cancer-related deaths worldwide. The discovery of robust diagnostic biomarkers for GC remains a challenge. This study sought to identify biomarker candidates for GC by integrating machine learning (ML) and bioinformatics approaches. Transcriptome profiles of patients with GC were analyzed to identify differentially expressed genes between the tumor and adjacent normal tissues. Subsequently, we constructed protein–protein interaction networks so as to find the significant hub genes. Along with the bioinformatics integration of ML methods such as support vector machine, the recursive feature elimination was used to select the most informative genes. The analysis unraveled 160 significant genes, with 88 upregulated and 72 downregulated, 10 hub genes, and 12 features from the variable selection method. The integrated analyses found that EXO1 , DTL , KIF14 , and TRIP13 genes are significant and poised as potential diagnostic biomarkers in relation to GC. The receiver operating characteristic curve analysis found KIF14 and TRIP13 are strongly associated with diagnosis of GC. We suggest KIF14 and TRIP13 are considered as biomarker candidates that might potentially inform future research on diagnosis, prognosis, or therapeutic targets for GC. These findings collectively offer new future possibilities for precision/personalized medicine research and development for patients with GC.

Patients with cancer are presumed to be vulnerable to an increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe clinical outcomes due to the immunocompromised state mediated by their underlying malignancies and therapy. The aim of this study was to estimate the SARS-CoV-2 seroprevalence, following second to fourth waves in solid tumour patients attending the Steve Biko Academic Hospital (SBAH) for diagnosis and treatment of cancer. We used the single-prick COVID-19 IgG/IgM Rapid Test Cassettes to detect SARS-CoV-2 IgG/IgM antibodies in 760 patients with solid tumours who were asymptomatic and who had never tested positive for coronavirus disease 2019 (COVID-19). Out of the 760 patients, 277 were male (36.4%), 483 were female (63.6%), and the mean age was 55 years (range 18–92). The estimated total seroprevalence was 33.2%. The seroprevalence status of the COVID-19 IgG/IgM antibodies rose significantly from the second wave (11.3%) to the third (67.38%) and then the fourth (69.81%) waves with roughly similar counts. A significant number of the seropositive patients were asymptomatic to COVID-19 (96%). There was a higher rate of seropositivity in cancer patients with hypertension (p < 0.05). Patients with breast, gynaecologic, and prostate cancers exhibited increased SARS-CoV-2 seropositivity. Although oncology patients may be susceptible to SARS-CoV-2 infection, our data indicate that these patients remained asymptomatic throughout various waves with an overall COVID-19 IgG/IgM antibody seropositivity of 33.16%, suggesting no risk of severe or fatal cases of COVID-19.