Bioinformatics analysis deciphering the transcriptomic signatures associated with signalling pathways and prognosis in the myelodysplastic syndromes

Tuerxun, Niluopaer; Wang, Jie; Zhao, Fang; Qin, Yuting; Wang, Huan; Chen, Rong; Jian-ping, Hao

doi:10.1080/16078454.2022.2029256

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…[27][28][29] Similarly, the other IRGMS mentioned in this study have also been shown to be strongly associated with various tumors in previous studies. [30][31][32][33][34][35][36][37][38][39][40][41] 5 Conclusion…”

Section: Discussionmentioning

confidence: 99%

Multi-database analysis showed that genes associated with inflammatory response are important for prognosis prediction of renal clear cell carcinoma

Xiao

Jiang

et al. 2023

Preprint

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Background Clear cell carcinoma of the kidney is the largest subtype of kidney tumor. Inflammatory responses are involved in all stages of the tumor. The relationship between genes related to inflammatory response and renal clear cell carcinoma is expected to help the diagnosis and treatment of tumor patients. Methods First, we obtained all the data needed for this study free of charge from a public database. After differential analysis and COX regression, we obtained genes that were used to build the model. In addition, data from multiple databases were included in this study. To make the data from different sources comparable, we standardize all the data using the SVA package. Next, through LASSO regression, we constructed a prognostic model of genes related to inflammation (IRGM). The model contains 10 gene model signatures related to the inflammatory response (IRGMS). The data used for modeling and internal validation came from the TCGA database and the GSE29609 dataset. Clear cell renal carcinoma data from the ICGC database will be used for external validation. Tumor data from E-MTAB-1980 cohort will provide an additional external validation. The GSE40453 dataset and the GSE53757 dataset will be used to verify the differential expression of IRGMS. The immunohistochemistry of IRGMS will be queried through the HPA database. After adequate validation of IRGM, we explored the application of IRGM in greater depth by constructing nomograms, pathway enrichment analysis, immunocorrelation analysis, drug susceptibility analysis, and subtype identification. Results IRGM can robustly predict the prognosis of patient samples with clear cell carcinoma of the kidney from different databases. IRGMS (IGFBP3, SCNN1B, IFI16, LRRC19, GSTM3, IFI44, APOLD1, HPGD, CPA3, PROM1) is expected to become a new biomarker associated with clear cell carcinoma of the kidney. The construction of nomogram can use IRGM to predict patient survival more accurately, so as to adopt more reasonable treatment methods. Pathway enrichment analysis showed that patients in the HR group were associated with a variety of tumorigenesis biological processes. Immune-related analysis and drug susceptibility analysis suggest that patients with higher IRGM scores have more treatment options. The subtype identification results are conducive to further refinement of treatment. Conclusion IRGMS (IGFBP3, SCNN1B, IFI16, LRRC19, GSTM3, IFI44, APOLD1, HPGD, CPA3, PROM1) is valuable in predicting the prognosis of clear cell carcinoma of the kidney. Patients with higher IRGM scores may be better candidates for treatment with immune checkpoint inhibitors and have more chemotherapy options.

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Section: Discussionmentioning

confidence: 99%

Multi-database analysis showed that genes associated with inflammatory response are important for prognosis prediction of renal clear cell carcinoma

Xiao

Jiang

et al. 2023

Preprint

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“…Recent work has explored the relationship between transcriptional signatures and critical signalling pathways to determine survival prognosis and diagnostic efficacy in MDS patient cohorts (26). However, no studies to date integrated clinical MDS phenotypes, RTEs expression, cell-type composition, and immune and aging gene signatures.…”

Section: Introductionmentioning

confidence: 99%

Identification of novel myelodysplastic syndromes prognostic subgroups by integration of inflammation, cell-type composition, and immune signatures in the bone marrow

Gerlevik,

Seymen,

Hama

et al. 2024

Preprint

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Mutational profiles of Myelodysplastic syndromes (MDS) have established that a relatively small number of genetic aberrations, including SF3B1 and SRSF2 spliceosome mutations, lead to specific phenotypes and prognostic subgrouping. We performed a Multi-Omics Factor Analysis (MOFA) on two published MDS cohorts of bone marrow mononuclear cells (BMMNCs) and CD34+ cells with three data modalities (clinical, genotype, and transcriptomics). Seven different views, including immune profile, inflammation/aging, Retrotransposon (RTE) expression, and cell-type composition, were derived from these modalities to identify the latent factors with significant impact on MDS prognosis. SF3B1 was the only mutation among 13 mutations in the BMMNC cohort, indicating a significant association with high inflammation. This trend was also observed to a lesser extent in the CD34+ cohort. Interestingly, the MOFA factor representing the inflammation shows a good prognosis for MDS patients with high inflammation. In contrast, SRSF2 mutant cases show a granulocyte-monocyte progenitor (GMP) pattern and high levels of senescence, immunosenescence, and malignant myeloid cells, consistent with their poor prognosis. Furthermore, MOFA identified RTEs expression as a risk factor for MDS. This work elucidates the efficacy of our integrative approach to assess the MDS risk that goes beyond all the scoring systems described thus far for MDS.

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Expression and clinical significance of RAG1 in myelodysplastic syndromes

et al. 2022

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Bioinformatics analysis deciphering the transcriptomic signatures associated with signalling pathways and prognosis in the myelodysplastic syndromes

Cited by 6 publications

References 40 publications

Multi-database analysis showed that genes associated with inflammatory response are important for prognosis prediction of renal clear cell carcinoma

Multi-database analysis showed that genes associated with inflammatory response are important for prognosis prediction of renal clear cell carcinoma

Identification of novel myelodysplastic syndromes prognostic subgroups by integration of inflammation, cell-type composition, and immune signatures in the bone marrow

Expression and clinical significance of RAG1 in myelodysplastic syndromes

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