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Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification
Breast cancer remains a significant health challenge worldwide, necessitating the identification of reliable biomarkers for early detection, accurate prognosis, and targeted therapy. Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DE...
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| Published in: | BMC cancer 2024-01, Vol.24 (1), p.155-13, Article 155 |
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| creator | Golestan, Ali Tahmasebi, Ahmad Maghsoodi, Nafiseh Faraji, Seyed Nooreddin Irajie, Cambyz Ramezani, Amin |
| description | Breast cancer remains a significant health challenge worldwide, necessitating the identification of reliable biomarkers for early detection, accurate prognosis, and targeted therapy.
Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers.
The study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples.
These findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer. |
| doi_str_mv | 10.1186/s12885-024-11913-7 |
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Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers.
The study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples.
These findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer.</description><identifier>ISSN: 1471-2407</identifier><identifier>EISSN: 1471-2407</identifier><identifier>DOI: 10.1186/s12885-024-11913-7</identifier><identifier>PMID: 38291367</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Analysis ; Bioinformatic analysis ; Bioinformatics ; Biological markers ; Biomarker identification ; Biomarkers ; Biomarkers, Tumor - genetics ; Breast cancer ; Breast Neoplasms - diagnosis ; Breast Neoplasms - genetics ; Care and treatment ; CEA (Oncology) ; Computational biology ; Computational Biology - methods ; Diagnosis ; Differentially expressed genes ; Female ; Gene Expression Regulation, Neoplastic ; Genes ; Genomics ; Humans ; Investigations ; Medical prognosis ; Membrane Proteins - genetics ; Mutation ; Online data bases ; Prognosis ; Protein Serine-Threonine Kinases - genetics ; Protein-Tyrosine Kinases - genetics ; qRT-PCR ; Survival analysis ; Testing ; Therapeutic targets ; Up-Regulation</subject><ispartof>BMC cancer, 2024-01, Vol.24 (1), p.155-13, Article 155</ispartof><rights>2024. The Author(s).</rights><rights>COPYRIGHT 2024 BioMed Central Ltd.</rights><rights>2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c629t-4d0eb6b973b6ab574ab87594a7c6ca8984fb8c452f8c0dc579e4044dfec938143</citedby><cites>FETCH-LOGICAL-c629t-4d0eb6b973b6ab574ab87594a7c6ca8984fb8c452f8c0dc579e4044dfec938143</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10829368/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2925595365?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25732,27903,27904,36991,36992,44569,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38291367$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Golestan, Ali</creatorcontrib><creatorcontrib>Tahmasebi, Ahmad</creatorcontrib><creatorcontrib>Maghsoodi, Nafiseh</creatorcontrib><creatorcontrib>Faraji, Seyed Nooreddin</creatorcontrib><creatorcontrib>Irajie, Cambyz</creatorcontrib><creatorcontrib>Ramezani, Amin</creatorcontrib><title>Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification</title><title>BMC cancer</title><addtitle>BMC Cancer</addtitle><description>Breast cancer remains a significant health challenge worldwide, necessitating the identification of reliable biomarkers for early detection, accurate prognosis, and targeted therapy.
Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers.
The study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples.
These findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer.</description><subject>Analysis</subject><subject>Bioinformatic analysis</subject><subject>Bioinformatics</subject><subject>Biological markers</subject><subject>Biomarker identification</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - diagnosis</subject><subject>Breast Neoplasms - genetics</subject><subject>Care and treatment</subject><subject>CEA (Oncology)</subject><subject>Computational biology</subject><subject>Computational Biology - methods</subject><subject>Diagnosis</subject><subject>Differentially expressed genes</subject><subject>Female</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Genes</subject><subject>Genomics</subject><subject>Humans</subject><subject>Investigations</subject><subject>Medical prognosis</subject><subject>Membrane Proteins - genetics</subject><subject>Mutation</subject><subject>Online data bases</subject><subject>Prognosis</subject><subject>Protein Serine-Threonine Kinases - genetics</subject><subject>Protein-Tyrosine Kinases - genetics</subject><subject>qRT-PCR</subject><subject>Survival analysis</subject><subject>Testing</subject><subject>Therapeutic targets</subject><subject>Up-Regulation</subject><issn>1471-2407</issn><issn>1471-2407</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkt-O1CAUxhujcdfVF_DCNDExetEVKBTwxmw2_plkExN1r8kppR3GFkZoJ7sP4HtLZ9Z1agwXnMDv-4CPk2XPMTrHWFRvIyZCsAIRWmAscVnwB9kpphwXhCL-8Kg-yZ7EuEEIc4HE4-ykFCTxFT_Nfl27nbG9dV2-DX6wca7qYCCOuQanTchr6wcIP0yI73JwuXWj6QKMdmdy2CYR6HWu_VBbt9dab13rw5AIHZMA-tto56LJzc3WBDsYN0Kf71LZWp0w755mj1roo3l2N59l1x8_fL_8XFx9-bS6vLgqdEXkWNAGmbqqJS_rCmrGKdSCM0mB60qDkIK2tdCUkVZo1GjGpaGI0qY1WpYC0_IsWx18Gw8btU13gXCrPFi1X_ChUxDSvXujiKg5QEVBAKOyJkBQIymudFMxIso2eb0_eG2nejCNTq8K0C9MlzvOrlXndwqjlH5ZieTw-s4h-J-TiaNK-WvT9-CMn6IikiDGseQ8oS__QTd-CinbPcWYZGXF_lIdpBfM35AO1rOpuuCCzE5kPvb8P1QajRms9s60Nq0vBG8WgsSM5mbsYIpRrb59XbKvjti1gX5cR99P8y_HJUgOoA4-xmDa--QwUnN7q0N7q9Teat_eao7hxXHm95I__Vz-Bq9b9oE</recordid><startdate>20240131</startdate><enddate>20240131</enddate><creator>Golestan, Ali</creator><creator>Tahmasebi, Ahmad</creator><creator>Maghsoodi, Nafiseh</creator><creator>Faraji, Seyed Nooreddin</creator><creator>Irajie, Cambyz</creator><creator>Ramezani, Amin</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</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>ISR</scope><scope>3V.</scope><scope>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20240131</creationdate><title>Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification</title><author>Golestan, Ali ; 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Breast cancer RNA expression data from the TCGA database were analyzed to identify differentially expressed genes (DEGs). The top 500 up-regulated DEGs were selected for further investigation using random forest analysis to identify important genes. These genes were evaluated based on their potential as diagnostic biomarkers, their overexpression in breast cancer tissues, and their low median expression in normal female tissues. Various validation methods, including online tools and quantitative Real-Time PCR (qRT-PCR), were used to confirm the potential of the identified genes as breast cancer biomarkers.
The study identified four overexpressed genes (CACNG4, PKMYT1, EPYC, and CHRNA6) among 100 genes with higher importance scores. qRT-PCR analysis confirmed the significant upregulation of these genes in breast cancer patients compared to normal samples.
These findings suggest that CACNG4, PKMYT1, EPYC, and CHRNA6 may serve as valuable biomarkers for breast cancer diagnosis, and PKMYT1 may also have prognostic significance. Furthermore, CACNG4, CHRNA6, and PKMYT1 show promise as potential therapeutic targets. These findings have the potential to advance diagnostic methods and therapeutic approaches for breast cancer.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>38291367</pmid><doi>10.1186/s12885-024-11913-7</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Bioinformatic analysis Bioinformatics Biological markers Biomarker identification Biomarkers Biomarkers, Tumor - genetics Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - genetics Care and treatment CEA (Oncology) Computational biology Computational Biology - methods Diagnosis Differentially expressed genes Female Gene Expression Regulation, Neoplastic Genes Genomics Humans Investigations Medical prognosis Membrane Proteins - genetics Mutation Online data bases Prognosis Protein Serine-Threonine Kinases - genetics Protein-Tyrosine Kinases - genetics qRT-PCR Survival analysis Testing Therapeutic targets Up-Regulation |
| title | Unveiling promising breast cancer biomarkers: an integrative approach combining bioinformatics analysis and experimental verification |
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