Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

Moon, Kyoung Min; Min, Kyueng‐Whan; Kim, Mi-Hye; Kim, Dong-Hoon; Son, Byoung Kwan; Oh, Young Ha; Jung, Woon-Yong; Kwon, Mijung; Kwon, O‐Yu

doi:10.3390/jcm8101580

Cited by 4 publications

(3 citation statements)

References 52 publications

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“…GSEA (http://softw are.broad insti tute.org/gsea/index .jsp) [58] explored the biological functions of the gene signature based on the energy metabolism status (high-risk vs. low-risk groups). The annotated gene set c2.cp.kegg.…”

Section: Gene Set Enrichment Analysis (Gsea)mentioning

confidence: 99%

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

Zhu

Hou²,

et al. 2020

Cancer Cell Int

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Background: Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occurring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown. Methods: Energy metabolism-related genes were obtained from the TARGET database. We applied the "NFM" algorithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identified by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selection operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model. Results: Two subtypes associated with energy metabolism was determined using the "NFM" algorithm, and significant modules related to energy metabolism were identified by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the significant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis. Upregulation of CCAR1, and C1QTNF1 was associated with augmented OS risk, whereas, increases in the expression SCL18B1, RBMXL1, DOK3, HS3ST2, and ATP6VOD1 was correlated with a diminished risk of OS. We confirmed that the seven-gene signature was robust, and was superior to the earlier models evaluated; therefore, it may be used for timely OS diagnosis, treatment, and prognosis. Conclusions: The seven-gene signature related to OS energy metabolism developed here could be used in the early diagnosis, treatment, and prognosis of OS.

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Section: Gene Set Enrichment Analysis (Gsea)mentioning

confidence: 99%

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

Zhu

Hou²,

et al. 2020

Cancer Cell Int

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“…The APACHE III scores of patients admitted to the ICU with melioidosis, leptospirosis, and rickettsial disease were compared with those of patients with sepsis due to other pathogens. The APACHE III scores also overestimated the case-fatality rates of patients admitted to the hospital's ICU with the other pathogens: the mean (95% CI) APACHE III score of these 2,092 patients was 75 (74)(75)(76). Their overall mean (95%) predicted case-fatality rate was 32 (31-24%) compared with the observed case-fatality rate of 301/2092 (14%).…”

Section: Resultsmentioning

confidence: 84%

The Applicability of Commonly Used Severity of Illness Scores to Tropical Infections in Australia

Salaveria

Smith

Liu

et al. 2022

The American Journal of Tropical Medicine and Hygiene

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Many patients with leptospirosis, melioidosis, and rickettsial infection require intensive care unit (ICU) admission in tropical Australia every year. The multi-organ dysfunction associated with these infections results in significantly elevated severity of illness (SOI) scores. However, the accuracy of these SOI scores in predicting death from these tropical infections is incompletely defined. This retrospective study was performed at Cairns Hospital, a tertiary-referral hospital in tropical Australia. All patients admitted to ICU with laboratory-confirmed leptospirosis, melioidosis, and rickettsial disease between January 1, 1999 and June 30, 2020, were eligible for the study. The ability of Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, Simplified Acute Physiology Scores (SAPS) II, and Sequential Organ Failure Assessment (SOFA) scores to predict death before ICU discharge was evaluated. Overall, 18 (12.1%) of the 149 included patients died: 15/74 (20.3%) with melioidosis, 2/54 (3.7%) with leptospirosis and 1/21 (4.8%) with rickettsial disease. However, the APACHE II, APACHE III, SAPS II, and SOFA scores significantly overestimated the case-fatality rate of all the infections; the disparity between the predicted and observed mortality was most marked in the cases of leptospirosis and rickettsial disease. Commonly used SOI scores significantly overestimate the case-fatality rate of melioidosis, leptospirosis, and rickettsial infections in Australian ICU patients. This may be at least partly explained by the unique pathophysiology of these infections, particularly leptospirosis and rickettsial disease. However, SOI scores may still be useful in facilitating the comparison of disease severity in clinical trials that examine patients with these pathogens.

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“…Although functional enrichment analysis is typically focused on determining the impact of experimental differences, sometimes researchers are interested in how transcriptomic variation is associated with health outcomes, such as survival 12,13 . Additionally, there is often an interest in which biological functions are associated with the outcome, rather than individual gene 14 . Even if a speci c gene is the cause of poor disease outcomes, an intervention might be possible at multiple points in the functional pathway involving that speci c gene.…”

Section: Introductionmentioning

confidence: 99%

An R package for Survival-based Gene Set Enrichment Analysis

Deng,

Thompson

2023

Preprint

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Functional enrichment analysis is usually used to assess the effects of experimental differences. However, researchers sometimes want to understand the relationship between transcriptomic variation and health outcomes like survival. Therefore, we suggest the use of Survival-based Gene Set Enrichment Analysis (SGSEA) to help determine biological functions associated with a disease’s survival. We developed an R package and corresponding Shiny App called SGSEA for this analysis and presented a study of kidney renal clear cell carcinoma (KIRC) to demonstrate the approach. In Gene Set Enrichment Analysis (GSEA), the log-fold change in expression between treatments is used to rank genes, to determine if a biological function has a non-random distribution of altered gene expression. SGSEA is a variation of GSEA using the hazard ratio instead of a log fold change. Our study shows that pathways enriched with genes whose increased transcription is associated with mortality (NES > 0, adjusted p-value < 0.15) have previously been linked to KIRC survival, helping to demonstrate the value of this approach. This approach allows researchers to quickly identify disease variant pathways for further research and provides supplementary information to standard GSEA, all within a single R package or through using the convenient app.

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Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

Cited by 4 publications

References 52 publications

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

The Applicability of Commonly Used Severity of Illness Scores to Tropical Infections in Australia

An R package for Survival-based Gene Set Enrichment Analysis

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