Gene set enrichment analysis identifies immune subtypes of kidney renal clear cell carcinoma with significantly different molecular and clinical properties.

Background: Kidney renal clear cell carcinoma (KIRC) is the most prevalent renal malignancy, marked by a high abundance of tumor-infiltrating lymphocytes (TILs) and an unfavorable prognosis upon metastasis. Numerous studies have demonstrated that KIRC possesses a tumor microenvironment that is highly heterogeneous, and this is associated with significant variations in the effectiveness of most first-line drugs administered to KIRC patients. Therefore, it is crucial to classify KIRC based on the tumor microenvironment, although these subtyping techniques are still inadequate. Methods: By applying gene set enrichment scores of 28 immune signatures, we conducted a hierarchical clustering of KIRC and determined its immune subtypes. In addition, we conducted a comprehensive exploration of the molecular and clinical features of these subtypes, including survival prognosis, proliferation, stemness, angiogenesis, tumor microenvironment, genome instability, intratumor heterogeneity, and pathway enrichment. Results: Through cluster analysis, two immune subtypes of KIRC were identified and termed Immunity-High (Immunity-H) and Immunity-Low (Immunity-L). This clustering outcome was consistent in four independent KIRC cohorts. The subtype Immunity-H exhibited elevated levels of TILs, tumor aneuploidy, homologous recombination deficiency, stemness, and proliferation potential, along with a poorer prognosis for survival. Despite this, the Immunity-L subtype demonstrated elevated intratumor heterogeneity and a stronger angiogenesis signature in contrast to Immunity-H. According to the results of pathway enrichment analysis, the Immunity-H subtype was found to be highly enriched in immunological, oncogenic, and metabolic pathways, whereas the Immunity-L subtype was highly enriched in angiogenic, neuroactive ligand-receptor interaction, and PPAR pathways. Conclusions: Based on the enrichment of immune signatures in the tumor microenvironment, KIRC can be categorized into two immune subtypes. The two subtypes demonstrate considerably distinct molecular and clinical features. In KIRC, an increase in immune infiltration is linked to a poor prognosis. Patients with Immunity-H KIRC may exhibit active responses to PPAR and immune checkpoint inhibitors, whereas patients with Immunity-L may manifest favorable responses to anti-angiogenic agents and immune checkpoint inhibitors. The immunological classification provides molecular insights into KIRC immunity, as well as clinical implications for the management of this disease.

Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data.

Background: Although several key molecules have been identified to modulate SARS-CoV-2 invasion of human host cells, the molecules correlated with outcomes in COVID-19 caused by SARS-CoV-2 infection remain insufficiently explored. Methods: This study analyzed three RNA-Seq gene expression profiling datasets for COVID-19 and identified differentially expressed genes (DEGs) between COVID-19 patients and normal people, commonly in the three datasets. Furthermore, this study explored the correlation between the expression of these genes and clinical features in COVID-19 patients. Results: This analysis identified 13 genes significantly upregulated in COVID-19 patients' leukocyte and SARS-CoV-2-infected nasopharyngeal tissue compared to normal tissue. These genes included OAS1, OAS2, OAS3, OASL, HERC6, SERPING1, IFI6, IFI44, IFI44L, CMPK2, RSAD2, EPSTI1, and CXCL10, all of which are involved in antiviral immune regulation. We found that these genes' downregulation was associated with worse clinical outcomes in COVID-19 patients, such as intensive care unit (ICU) admission, mechanical ventilatory support (MVS) requirement, elevated D-dimer levels, and increased viral loads. Furthermore, this analysis identified two COVID-19 clusters based on the expression profiles of the 13 genes, termed COV-C1 and COV-C2. Compared with COV-C1, COV-C2 more highly expressed the 13 genes, had stronger antiviral immune responses, were younger, and displayed more favorable clinical outcomes. Conclusions: A strong antiviral immune response is essential in reducing severity of COVID-19.

Computational analysis of TMPRSS2 expression in normal and SARS-CoV-2-infected human tissues.

The transmembrane serine protease 2 (TMPRSS2) is a key molecule for SARS-CoV-2 invading human host cells. To provide insights into SARS-CoV-2 infection of various human tissues and understand the potential mechanism of SARS-CoV-2 infection, we investigated TMPRSS2 expression in various normal human tissues and SARS-CoV-2-infected human tissues. Using publicly available datasets, we performed computational analyses of TMPRSS2 expression levels in 30 normal human tissues, and compared them between males and females and between younger (ages ≤ 49 years) and older (ages > 49 years) populations in these tissues. We found that TMPRSS2 expression levels were the highest in the prostate, stomach, pancreas, lungs, small intestine, and salivary gland. The TMPRSS2 protein had relatively high expression levels in the parathyroid gland, stomach, small intestine, pancreas, kidneys, seminal vesicle, epididymis, and prostate. However, TMPRSS2 expression levels were not significantly different between females and males or between younger and older populations in these tissues. The pathways enriched in TMPRSS2-upregulated pan-tissue were mainly involved in immune, metabolism, cell growth and proliferation, stromal signatures, and cancer and other diseases. Many cytokine genes displayed positive expression correlations with TMPRSS2 in pan-tissue, including TNF-α, IL-1, IL-2, IL-4, IL-7, IL-8, IL-12, IL-18, IFN-α, MCP-1, G-CSF, and IP-10. We further analyzed TMPRSS2 expression levels in nasopharyngeal swabs from SARS-CoV-2-infected patients. TMPRSS2 expression levels showed no significant difference between males and females or between younger and older patients. However, they were significantly lower in SARS-CoV-2-infected than in healthy individuals and patients with other viral acute respiratory illnesses. Interestingly, TMPRSS2 expression levels were positively correlated with the enrichment levels of four immune signatures (B cells, CD8+ T cells, NK cells, and interferon response) in SARS-CoV-2-infected patients but likely to be negatively correlated with them in the normal lung tissue. Our data may provide insights into the mechanism of SARS-CoV-2 infection.

Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach.

The COVID-19 virus has infected more than 38 million people and resulted in more than one million deaths worldwide as of October 14, 2020. By using the logistic regression model, we identified novel critical factors associated with COVID19 cases, death, and case fatality rates in 154 countries and in the 50 U.S. states. Among numerous factors associated with COVID-19 risk, economic inequality enhanced the risk of COVID-19 transmission. The per capita hospital beds correlated negatively with COVID-19 deaths. Blood types B and AB were protective factors for COVID-19 risk, while blood type A was a risk factor. The prevalence of HIV and influenza and pneumonia was associated with reduced COVID-19 risk. Increased intake of vegetables, edible oil, protein, vitamin D, and vitamin K was associated with reduced COVID-19 risk, while increased intake of alcohol was associated with increased COVID-19 risk. Other factors included age, sex, temperature, humidity, social distancing, smoking, health investment, urbanization level, and race. High temperature is a more compelling factor mitigating COVID-19 transmission than low temperature. Our comprehensive identification of the factors affecting COVID-19 transmission and fatality may provide new insights into the COVID-19 pandemic and advise effective strategies for preventing and migrating COVID-19 spread.