Identifying the Effect of COVID-19 Infection in Multiple Myeloma and Diffuse Large B-Cell Lymphoma Patients Using Bioinformatics and System Biology

GPX4 is a potential diagnostic and therapeutic biomarker associated with diffuse large B lymphoma cell proliferation and B cell immune infiltration

At present, GPX4's role in the occurrence and development of diffuse large B lymphoma (DLBCL) is rarely reported. This study's purpose is to explore GPX4's significance in the diagnosis, treatment, and pathological mechanisms of DLBCL. The TIMER 2.0, GEPIA, and GEO databases were used to analyze GPX4's expression levels in DLBCL tissue, peripheral blood, and single cells, and evaluate its potential performance as a therapeutic and diagnostic marker. Cell experiments validate GPX4's role in DLBCL cells. And revealed the potential mechanism of GPX4's action from three aspects: immunity, pathogenic gene expression, and protein interaction. The results indicate that GPX4 can be used as a biomarker for treatment and diagnosis (FC > 1.5, P < 0.05, AUC>0.8, KM-P value < 0.05). In single cell data, GPX4 also showed high expression in immune cells. Besides, cell experiments have confirmed that GPX4's high expression can inhibit DLBCL cells' proliferation. Meanwhile, we found a negative correlation between GPX4 and the 16 core DLBCL's pathogenic genes, and a significant negative correlation with immune B cell infiltration. In summary, GPX4 can serve as a potential therapeutic and diagnostic marker for DLBCL. GPX4's high expression can lead to a good prognosis in DLBCL patients, which may be related to its inhibition of cancer cell proliferation, high expression of key pathogenic genes, and infiltration of immune B cells.

Integration of Omics Data and Network Models to Unveil Negative Aspects of SARS-CoV-2, from Pathogenic Mechanisms to Drug Repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 health emergency, affecting and killing millions of people worldwide. Following SARS-CoV-2 infection, COVID-19 patients show a spectrum of symptoms ranging from asymptomatic to very severe manifestations. In particular, bronchial and pulmonary cells, involved at the initial stage, trigger a hyper-inflammation phase, damaging a wide range of organs, including the heart, brain, liver, intestine and kidney. Due to the urgent need for solutions to limit the virus' spread, most efforts were initially devoted to mapping outbreak trajectories and variant emergence, as well as to the rapid search for effective therapeutic strategies. Samples collected from hospitalized or dead COVID-19 patients from the early stages of pandemic have been analyzed over time, and to date they still represent an invaluable source of information to shed light on the molecular mechanisms underlying the organ/tissue damage, the knowledge of which could offer new opportunities for diagnostics and therapeutic designs. For these purposes, in combination with clinical data, omics profiles and network models play a key role providing a holistic view of the pathways, processes and functions most affected by viral infection. In fact, in addition to epidemiological purposes, networks are being increasingly adopted for the integration of multiomics data, and recently their use has expanded to the identification of drug targets or the repositioning of existing drugs. These topics will be covered here by exploring the landscape of SARS-CoV-2 survey-based studies using systems biology approaches derived from omics data, paying particular attention to those that have considered samples of human origin.

Risk and Severity of COVID-19 Infection in Monoclonal Gammopathy of Undetermined Significance: A 3-Year Propensity Matched Cohort Study

INTRODUCTION

Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant disorder causing monoclonal plasma cell proliferation in bone marrow. This population is at risk of developing multiple myeloma (MM) and severe viral infections; risk factors of severe COVID-19 infection. Using TriNetX, a global platform providing data of 120 million patients, we aimed to quantify the risk and severity of COVID-19 in MGUS patients.

PATIENTS AND METHODS

A retrospective cohort analysis was performed using the TriNetX Global Collaborative Network. From January 20, 2020, to January 20, 2023, we identified a cohort of 58,859 MGUS patients and compared to non-MGUS patients, determined by relevant diagnosis/LOINC test codes. After 1:1 propensity score-matching, we identified COVID-19 cases to quantify risk and identify patients who had been hospitalized, ventilated/intubated, and deceased to quantify severity. Measures of association and Kaplan-Meier analysis were conducted.

RESULTS

After propensity-score matching, there were 58,668 patients in both cohorts. MGUS patients were found to be at a reduced risk of contracting COVID-19 (RR 0.88, 95% CI 0.85-0.91). MGUS patients with COVID-19 showed higher mortality risk and decreased survival time compared to the general population (HR 1.14, 95% CI 1.01-1.27). MGUS patients with COVID-19 who were hospitalized exhibited significantly decreased survival time (log-rank test, P = 0.04).

CONCLUSION

As COVID-19 remains a looming health concern, especially amongst vulnerable populations, our analysis emphasizes the need for adequate vaccination and treatment regimens as well as an understanding of the severity of infection in MGUS patients and justification for precautionary measures.