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Identification and prognostic analysis of metabolic genes associated with TP53 mutation in multiple myeloma
TP53 gene mutation is crucial in determining the prognosis of Multiple Myeloma (MM) patients. Understanding metabolic genes linked to TP53 mutation is vital for developing targeted therapies for these patients. We analyzed The Cancer Genome Atlas (TCGA) dataset to identify genes related to TP53 muta...
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| Published in: | Hematology (Luxembourg) 2024-12, Vol.29 (1), p.2377850 |
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| creator | Tang, Xiaoyan Chen, Yongping |
| description | TP53 gene mutation is crucial in determining the prognosis of Multiple Myeloma (MM) patients. Understanding metabolic genes linked to TP53 mutation is vital for developing targeted therapies for these patients.
We analyzed The Cancer Genome Atlas (TCGA) dataset to identify genes related to TP53 mutation and metabolism. Using univariate Cox regression and protein-protein interaction (PPI) analysis, we identified key genes. We categorized patients into high and low metabolism groups via non-negative matrix factorization (NMF) clustering, which led to the discovery of relevant differential genes. Integrating these with genes from the Gene Expression Omnibus (GEO) datasets and PPI interactions, we pinpointed crucial metabolic genes associated with TP53 mutation in MM. Additionally, we conducted prognostic analyses involving survival curves and receiver operating characteristic (ROC) charts.
Our study reveals that the metabolic gene ribonucleotide reductase M2 (RRM2), linked to TP53 mutation, correlates positively with the International Staging System (ISS) stage in MM patients and is an independent prognostic risk factor. In the TCGA dataset, among the 767 patients, the 35 MM patients with TP53 mutation generally had poor survival outcomes. Specifically, the patients with both TP53 mutation and high RRM2 expression had a 2-year survival rate of only 38.87%, whereas those with normal TP53 function and low RRM2 expression had a 2-year survival rate of 86.31% (
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| doi_str_mv | 10.1080/16078454.2024.2377850 |
| format | article |
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We analyzed The Cancer Genome Atlas (TCGA) dataset to identify genes related to TP53 mutation and metabolism. Using univariate Cox regression and protein-protein interaction (PPI) analysis, we identified key genes. We categorized patients into high and low metabolism groups via non-negative matrix factorization (NMF) clustering, which led to the discovery of relevant differential genes. Integrating these with genes from the Gene Expression Omnibus (GEO) datasets and PPI interactions, we pinpointed crucial metabolic genes associated with TP53 mutation in MM. Additionally, we conducted prognostic analyses involving survival curves and receiver operating characteristic (ROC) charts.
Our study reveals that the metabolic gene ribonucleotide reductase M2 (RRM2), linked to TP53 mutation, correlates positively with the International Staging System (ISS) stage in MM patients and is an independent prognostic risk factor. In the TCGA dataset, among the 767 patients, the 35 MM patients with TP53 mutation generally had poor survival outcomes. Specifically, the patients with both TP53 mutation and high RRM2 expression had a 2-year survival rate of only 38.87%, whereas those with normal TP53 function and low RRM2 expression had a 2-year survival rate of 86.31% (
< 0.001).
RRM2 significantly impacts MM prognosis and is associated with TP53 mutation, presenting itself as a potential therapeutic target and prognostic marker for MM.</description><identifier>ISSN: 1607-8454</identifier><identifier>EISSN: 1607-8454</identifier><identifier>DOI: 10.1080/16078454.2024.2377850</identifier><identifier>PMID: 39012217</identifier><language>eng</language><publisher>England: Taylor & Francis Group</publisher><subject>bioinformatics ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Male ; Middle Aged ; Multiple myeloma ; Multiple Myeloma - genetics ; Multiple Myeloma - mortality ; Mutation ; Prognosis ; Ribonucleoside Diphosphate Reductase - genetics ; RRM2 ; TP53 ; Tumor Suppressor Protein p53 - genetics</subject><ispartof>Hematology (Luxembourg), 2024-12, Vol.29 (1), p.2377850</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c300t-9ed6454f0e3616642e3ae421d5b7076679f50bd20625e97e436112fd3d79f6273</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39012217$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tang, Xiaoyan</creatorcontrib><creatorcontrib>Chen, Yongping</creatorcontrib><title>Identification and prognostic analysis of metabolic genes associated with TP53 mutation in multiple myeloma</title><title>Hematology (Luxembourg)</title><addtitle>Hematology</addtitle><description>TP53 gene mutation is crucial in determining the prognosis of Multiple Myeloma (MM) patients. Understanding metabolic genes linked to TP53 mutation is vital for developing targeted therapies for these patients.
We analyzed The Cancer Genome Atlas (TCGA) dataset to identify genes related to TP53 mutation and metabolism. Using univariate Cox regression and protein-protein interaction (PPI) analysis, we identified key genes. We categorized patients into high and low metabolism groups via non-negative matrix factorization (NMF) clustering, which led to the discovery of relevant differential genes. Integrating these with genes from the Gene Expression Omnibus (GEO) datasets and PPI interactions, we pinpointed crucial metabolic genes associated with TP53 mutation in MM. Additionally, we conducted prognostic analyses involving survival curves and receiver operating characteristic (ROC) charts.
Our study reveals that the metabolic gene ribonucleotide reductase M2 (RRM2), linked to TP53 mutation, correlates positively with the International Staging System (ISS) stage in MM patients and is an independent prognostic risk factor. In the TCGA dataset, among the 767 patients, the 35 MM patients with TP53 mutation generally had poor survival outcomes. Specifically, the patients with both TP53 mutation and high RRM2 expression had a 2-year survival rate of only 38.87%, whereas those with normal TP53 function and low RRM2 expression had a 2-year survival rate of 86.31% (
< 0.001).
RRM2 significantly impacts MM prognosis and is associated with TP53 mutation, presenting itself as a potential therapeutic target and prognostic marker for MM.</description><subject>bioinformatics</subject><subject>Female</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Multiple myeloma</subject><subject>Multiple Myeloma - genetics</subject><subject>Multiple Myeloma - mortality</subject><subject>Mutation</subject><subject>Prognosis</subject><subject>Ribonucleoside Diphosphate Reductase - genetics</subject><subject>RRM2</subject><subject>TP53</subject><subject>Tumor Suppressor Protein p53 - genetics</subject><issn>1607-8454</issn><issn>1607-8454</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpNUU1v3CAQRVWr5vMntOLYyyYDGLCPVdSPlSKlh_SMxma8JcVma1hV--_LdjdRLjDzZt6bgcfYBwE3Alq4FQZs2-jmRoKsh7K21fCGnR_w1aHw9lV8xi5yfgKQEiy8Z2eqAyGlsOfs99rTXMIYBiwhzRxnz7dL2swplzDUFOM-h8zTyCcq2KdY0Q3NlDnmnIaAhTz_G8ov_vhDKz7tylEozDWOJWwj8WlPMU14xd6NGDNdn-5L9vPrl8e776v7h2_ru8_3q0EBlFVH3tSdRyBlhDGNJIXUSOF1b8EaY7tRQ-8lGKmps9TUNiFHr3ytGGnVJVsfdX3CJ7ddwoTL3iUM7j-Qlo3Dpb4uktPUCi976lttmlbKjkQ3tNDjAIgdUtX6dNSqn_JnR7m4KeSBYsSZ0i47Ba2oW1dubdXH1mFJOS80vowW4A6euWfP3MEzd_Ks8j6eRuz6ifwL69kk9Q8HdpGg</recordid><startdate>202412</startdate><enddate>202412</enddate><creator>Tang, Xiaoyan</creator><creator>Chen, Yongping</creator><general>Taylor & Francis Group</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>DOA</scope></search><sort><creationdate>202412</creationdate><title>Identification and prognostic analysis of metabolic genes associated with TP53 mutation in multiple myeloma</title><author>Tang, Xiaoyan ; Chen, Yongping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c300t-9ed6454f0e3616642e3ae421d5b7076679f50bd20625e97e436112fd3d79f6273</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>bioinformatics</topic><topic>Female</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Multiple myeloma</topic><topic>Multiple Myeloma - genetics</topic><topic>Multiple Myeloma - mortality</topic><topic>Mutation</topic><topic>Prognosis</topic><topic>Ribonucleoside Diphosphate Reductase - genetics</topic><topic>RRM2</topic><topic>TP53</topic><topic>Tumor Suppressor Protein p53 - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tang, Xiaoyan</creatorcontrib><creatorcontrib>Chen, Yongping</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Hematology (Luxembourg)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tang, Xiaoyan</au><au>Chen, Yongping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification and prognostic analysis of metabolic genes associated with TP53 mutation in multiple myeloma</atitle><jtitle>Hematology (Luxembourg)</jtitle><addtitle>Hematology</addtitle><date>2024-12</date><risdate>2024</risdate><volume>29</volume><issue>1</issue><spage>2377850</spage><pages>2377850-</pages><issn>1607-8454</issn><eissn>1607-8454</eissn><abstract>TP53 gene mutation is crucial in determining the prognosis of Multiple Myeloma (MM) patients. Understanding metabolic genes linked to TP53 mutation is vital for developing targeted therapies for these patients.
We analyzed The Cancer Genome Atlas (TCGA) dataset to identify genes related to TP53 mutation and metabolism. Using univariate Cox regression and protein-protein interaction (PPI) analysis, we identified key genes. We categorized patients into high and low metabolism groups via non-negative matrix factorization (NMF) clustering, which led to the discovery of relevant differential genes. Integrating these with genes from the Gene Expression Omnibus (GEO) datasets and PPI interactions, we pinpointed crucial metabolic genes associated with TP53 mutation in MM. Additionally, we conducted prognostic analyses involving survival curves and receiver operating characteristic (ROC) charts.
Our study reveals that the metabolic gene ribonucleotide reductase M2 (RRM2), linked to TP53 mutation, correlates positively with the International Staging System (ISS) stage in MM patients and is an independent prognostic risk factor. In the TCGA dataset, among the 767 patients, the 35 MM patients with TP53 mutation generally had poor survival outcomes. Specifically, the patients with both TP53 mutation and high RRM2 expression had a 2-year survival rate of only 38.87%, whereas those with normal TP53 function and low RRM2 expression had a 2-year survival rate of 86.31% (
< 0.001).
RRM2 significantly impacts MM prognosis and is associated with TP53 mutation, presenting itself as a potential therapeutic target and prognostic marker for MM.</abstract><cop>England</cop><pub>Taylor & Francis Group</pub><pmid>39012217</pmid><doi>10.1080/16078454.2024.2377850</doi><oa>free_for_read</oa></addata></record> |
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| source | Taylor & Francis Open Access Journals |
| subjects | bioinformatics Female Gene Expression Regulation, Neoplastic Humans Male Middle Aged Multiple myeloma Multiple Myeloma - genetics Multiple Myeloma - mortality Mutation Prognosis Ribonucleoside Diphosphate Reductase - genetics RRM2 TP53 Tumor Suppressor Protein p53 - genetics |
| title | Identification and prognostic analysis of metabolic genes associated with TP53 mutation in multiple myeloma |
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