tetano
Editor, Senior Moderator
Comput Biol Med
. 2023 Dec 22:169:107883.
doi: 10.1016/j.compbiomed.2023.107883. Online ahead of print. Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods
Jingxin Ren[SUP] 1 [/SUP], XianChao Zhou[SUP] 2 [/SUP], Ke Huang[SUP] 3 [/SUP], Lei Chen[SUP] 4 [/SUP], Wei Guo[SUP] 5 [/SUP], KaiYan Feng[SUP] 6 [/SUP], Tao Huang[SUP] 7 [/SUP], Yu-Dong Cai[SUP] 8 [/SUP]
Affiliations
COVID-19 is hypothesized to exert enduring effects on the immune systems of patients, leading to alterations in immune-related gene expression. This study aimed to scrutinize the persistent implications of SARS-CoV-2 infection on gene expression and its influence on subsequent immune activation responses. We designed a machine learning-based approach to analyze transcriptomic data from both healthy individuals and patients who had recovered from COVID-19. Patients were categorized based on their influenza vaccination status and then compared with healthy controls. The initial sample set encompassed 86 blood samples from healthy controls and 72 blood samples from recuperated COVID-19 patients prior to influenza vaccination. The second sample set included 123 blood samples from healthy controls and 106 blood samples from recovered COVID-19 patients who had been vaccinated against influenza. For each sample, the dataset captured expression levels of 17,060 genes. Above two sample sets were first analyzed by seven feature ranking algorithms, yielding seven feature lists for each dataset. Then, each list was fed into the incremental feature selection method, incorporating three classic classification algorithms, to extract essential genes, classification rules and build efficient classifiers. The genes and rules were analyzed in this study. The main findings included that NEXN and ZNF354A were highly expressed in recovered COVID-19 patients, whereas MKI67 and GZMB were highly expressed in patients with secondary immune activation post-COVID-19 recovery. These pivotal genes could provide valuable insights for future health monitoring of COVID-19 patients and guide the creation of continued treatment regimens.
Keywords: COVID-19 recovery; Immune changes; Influenza vaccine; Machine learning.
. 2023 Dec 22:169:107883.
doi: 10.1016/j.compbiomed.2023.107883. Online ahead of print. Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods
Jingxin Ren[SUP] 1 [/SUP], XianChao Zhou[SUP] 2 [/SUP], Ke Huang[SUP] 3 [/SUP], Lei Chen[SUP] 4 [/SUP], Wei Guo[SUP] 5 [/SUP], KaiYan Feng[SUP] 6 [/SUP], Tao Huang[SUP] 7 [/SUP], Yu-Dong Cai[SUP] 8 [/SUP]
Affiliations
- PMID: 38157776
- DOI: 10.1016/j.compbiomed.2023.107883
COVID-19 is hypothesized to exert enduring effects on the immune systems of patients, leading to alterations in immune-related gene expression. This study aimed to scrutinize the persistent implications of SARS-CoV-2 infection on gene expression and its influence on subsequent immune activation responses. We designed a machine learning-based approach to analyze transcriptomic data from both healthy individuals and patients who had recovered from COVID-19. Patients were categorized based on their influenza vaccination status and then compared with healthy controls. The initial sample set encompassed 86 blood samples from healthy controls and 72 blood samples from recuperated COVID-19 patients prior to influenza vaccination. The second sample set included 123 blood samples from healthy controls and 106 blood samples from recovered COVID-19 patients who had been vaccinated against influenza. For each sample, the dataset captured expression levels of 17,060 genes. Above two sample sets were first analyzed by seven feature ranking algorithms, yielding seven feature lists for each dataset. Then, each list was fed into the incremental feature selection method, incorporating three classic classification algorithms, to extract essential genes, classification rules and build efficient classifiers. The genes and rules were analyzed in this study. The main findings included that NEXN and ZNF354A were highly expressed in recovered COVID-19 patients, whereas MKI67 and GZMB were highly expressed in patients with secondary immune activation post-COVID-19 recovery. These pivotal genes could provide valuable insights for future health monitoring of COVID-19 patients and guide the creation of continued treatment regimens.
Keywords: COVID-19 recovery; Immune changes; Influenza vaccine; Machine learning.