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Inflammation plays a vital role in cancer genesis and progression. However, the relationship between inflammatory response-related genes (IRRGs) and the prognosis of MM patients remains unknown. We constructed a IRRGs prognosis model by least absolute shrinkage and selection operator regression analysis. Moreover, clinical multivariate regression was performed to identify clinical implications. Gene set enrichment analysis was implemented to conduct its biological properties. CIBERSORT deconvolution algorithm was utilized to calculate the immune cell infiltration in different risk groups. The flow cytometry was utilized to perform protein expression of prognostic gene. A Six-IRRGs (VCAM1, RGS1, KIT, CD81, BLNK, and BIRC3) prognostic risk model was successfully constructed and validated. The risk model was an independent predictor for overall survival. Enrichment analysis revealed autophagy and PI3K\u2013Akt signaling pathways were enriched in the high-risk group. Furthermore, we found CD81 widely impacted on the infiltration of immune cells, especially on monocytes and macrophages2. At last, the role of CD81 in MM was confirmed to be an adverse prognostic factor in clinical. Our study explores the potential application value of IRRGs in MM. These findings may provide new insights into the treatment for MM patients.<\/jats:p>","DOI":"10.1007\/s10238-023-01277-w","type":"journal-article","created":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T11:02:21Z","timestamp":1706353341000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An inflammatory response-related gene signature can predict the prognosis and impact the immune infiltration of multiple myeloma"],"prefix":"10.1007","volume":"24","author":[{"given":"Qian","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,27]]},"reference":[{"key":"1277_CR1","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/S0140-6736(21)00135-5","volume":"397","author":"NWCJ van de Donk","year":"2021","unstructured":"van de Donk NWCJ, Pawlyn C, Yong KL. 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