The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk
It is widely hypothesized that the interactions of multiple genes influence individual risk to prostate cancer. However, current efforts at identifying prostate cancer risk genes primarily rely on single-gene approaches. In an attempt to fill this gap, we carried out a study to explore the joint eff...
Gespeichert in:
| Veröffentlicht in: | Cancer epidemiology, biomarkers & prevention biomarkers & prevention, 2005-11, Vol.14 (11), p.2563-2568 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2568 |
|---|---|
| container_issue | 11 |
| container_start_page | 2563 |
| container_title | Cancer epidemiology, biomarkers & prevention |
| container_volume | 14 |
| creator | JIANFENG XU LOWEY, James ADAMI, Hans-Olov SUH, Edward MOORE, Jason H ZHENG, S. Lilly ISAACS, William B TRENT, Jeffrey M GRÖNBERG, Henrik WIKLUND, Fredrik JIELIN SUN LINDMARK, Fredrik HSU, Fang-Chi DIMITROV, Latchezar BAOLI CHANG TURNER, Aubrey R WENNAN LIU |
| description | It is widely hypothesized that the interactions of multiple genes influence individual risk to prostate cancer. However, current
efforts at identifying prostate cancer risk genes primarily rely on single-gene approaches. In an attempt to fill this gap,
we carried out a study to explore the joint effect of multiple genes in the inflammation pathway on prostate cancer risk.
We studied 20 genes in the Toll-like receptor signaling pathway as well as several cytokines. For each of these genes, we
selected and genotyped haplotype-tagging single nucleotide polymorphisms (SNP) among 1,383 cases and 780 controls from the
CAPS ( CA ncer P rostate in S weden) study population. A total of 57 SNPs were included in the final analysis. A data mining method, multifactor dimensionality
reduction, was used to explore the interaction effects of SNPs on prostate cancer risk. Interaction effects were assessed
for all possible n SNP combinations, where n = 2, 3, or 4. For each n SNP combination, the model providing lowest prediction error among 100 cross-validations was chosen. The statistical significance
levels of the best models in each n SNP combination were determined using permutation tests. A four-SNP interaction (one SNP each from IL-10, IL-1RN, TIRAP , and TLR5 ) had the lowest prediction error (43.28%, P = 0.019). Our ability to analyze a large number of SNPs in a large sample size is one of the first efforts in exploring the
effect of high-order gene-gene interactions on prostate cancer risk, and this is an important contribution to this new and
quickly evolving field. |
| doi_str_mv | 10.1158/1055-9965.EPI-05-0356 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_swepu</sourceid><recordid>TN_cdi_swepub_primary_oai_swepub_ki_se_578181</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>68795914</sourcerecordid><originalsourceid>FETCH-LOGICAL-c525t-ce6518fed798cd33f073c171bf94fc14158fc004c643ae74773a05afb76441213</originalsourceid><addsrcrecordid>eNqFkl9v0zAUxSMEYmPwEUB-AQmJDDu24_hxKtuoNIkKBq-W61w3ZvlTbEdVvz1Om9GniSdfWb9zfa_PybK3BF8SwqvPBHOeS1nyy-vVMsc8x5SXz7JzwmmVC8H581Q_MmfZqxB-Y4yF5PxldkbKomJUyPMM7htAyz6C1ya6oUeDRTfD6NEt9BCQ61E8ALbVXacPxErHZqf36Ifb9M46o_vY7tHKQ-1MDKkYQtQR0EL3Bjz67sLD6-yF1W2AN_N5kf28ub5ffM3vvt0uF1d3ueEFj7mBkpPKQi1kZWpKLRbUEEHWVjJrCEt7W4MxMyWjGgQTgmrMtV2LkjFSEHqR5ce-YQfbca223nXa79WgnZqvHlIFiouKVBMvnuS3fqhPokchkZwVvEjKT08qv7hfV2rwGzV2oyJMYJrwD0c8df0zQoiqc8FA2-oehjGoskrOSML-CxJJRSnLKoH8CJr038GD_TcCwWpKiJrcV5P7KiVEYa6mhCTdu_mBcd1BfVLNkUjA-xnQwejW-mSjCydOFIJSOjX6eOQat2l2zoMyB8M9BNDeNGnzNIcq0pv0L5321BY</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>19376968</pqid></control><display><type>article</type><title>The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>American Association for Cancer Research</source><creator>JIANFENG XU ; LOWEY, James ; ADAMI, Hans-Olov ; SUH, Edward ; MOORE, Jason H ; ZHENG, S. Lilly ; ISAACS, William B ; TRENT, Jeffrey M ; GRÖNBERG, Henrik ; WIKLUND, Fredrik ; JIELIN SUN ; LINDMARK, Fredrik ; HSU, Fang-Chi ; DIMITROV, Latchezar ; BAOLI CHANG ; TURNER, Aubrey R ; WENNAN LIU</creator><creatorcontrib>JIANFENG XU ; LOWEY, James ; ADAMI, Hans-Olov ; SUH, Edward ; MOORE, Jason H ; ZHENG, S. Lilly ; ISAACS, William B ; TRENT, Jeffrey M ; GRÖNBERG, Henrik ; WIKLUND, Fredrik ; JIELIN SUN ; LINDMARK, Fredrik ; HSU, Fang-Chi ; DIMITROV, Latchezar ; BAOLI CHANG ; TURNER, Aubrey R ; WENNAN LIU</creatorcontrib><description>It is widely hypothesized that the interactions of multiple genes influence individual risk to prostate cancer. However, current
efforts at identifying prostate cancer risk genes primarily rely on single-gene approaches. In an attempt to fill this gap,
we carried out a study to explore the joint effect of multiple genes in the inflammation pathway on prostate cancer risk.
We studied 20 genes in the Toll-like receptor signaling pathway as well as several cytokines. For each of these genes, we
selected and genotyped haplotype-tagging single nucleotide polymorphisms (SNP) among 1,383 cases and 780 controls from the
CAPS ( CA ncer P rostate in S weden) study population. A total of 57 SNPs were included in the final analysis. A data mining method, multifactor dimensionality
reduction, was used to explore the interaction effects of SNPs on prostate cancer risk. Interaction effects were assessed
for all possible n SNP combinations, where n = 2, 3, or 4. For each n SNP combination, the model providing lowest prediction error among 100 cross-validations was chosen. The statistical significance
levels of the best models in each n SNP combination were determined using permutation tests. A four-SNP interaction (one SNP each from IL-10, IL-1RN, TIRAP , and TLR5 ) had the lowest prediction error (43.28%, P = 0.019). Our ability to analyze a large number of SNPs in a large sample size is one of the first efforts in exploring the
effect of high-order gene-gene interactions on prostate cancer risk, and this is an important contribution to this new and
quickly evolving field.</description><identifier>ISSN: 1055-9965</identifier><identifier>EISSN: 1538-7755</identifier><identifier>DOI: 10.1158/1055-9965.EPI-05-0356</identifier><identifier>PMID: 16284379</identifier><language>eng</language><publisher>Philadelphia, PA: American Association for Cancer Research</publisher><subject>association ; Biological and medical sciences ; Case-Control Studies ; data mining ; Genetic Predisposition to Disease ; Genotype ; Haplotypes ; Humans ; Inflammation ; Male ; MDR ; Medical sciences ; Medicin och hälsovetenskap ; Nephrology. Urinary tract diseases ; Polymorphism, Single Nucleotide ; Prognosis ; prostate cancer ; Prostatic Neoplasms - etiology ; Prostatic Neoplasms - genetics ; Prostatic Neoplasms - immunology ; Registries - statistics & numerical data ; Risk Factors ; Signal Transduction ; SNPs ; Toll-Like Receptors - genetics ; Tumors ; Tumors of the urinary system ; Urinary tract. Prostate gland</subject><ispartof>Cancer epidemiology, biomarkers & prevention, 2005-11, Vol.14 (11), p.2563-2568</ispartof><rights>2006 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c525t-ce6518fed798cd33f073c171bf94fc14158fc004c643ae74773a05afb76441213</citedby><cites>FETCH-LOGICAL-c525t-ce6518fed798cd33f073c171bf94fc14158fc004c643ae74773a05afb76441213</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,3356,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17273336$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16284379$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-14703$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:1954252$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>JIANFENG XU</creatorcontrib><creatorcontrib>LOWEY, James</creatorcontrib><creatorcontrib>ADAMI, Hans-Olov</creatorcontrib><creatorcontrib>SUH, Edward</creatorcontrib><creatorcontrib>MOORE, Jason H</creatorcontrib><creatorcontrib>ZHENG, S. Lilly</creatorcontrib><creatorcontrib>ISAACS, William B</creatorcontrib><creatorcontrib>TRENT, Jeffrey M</creatorcontrib><creatorcontrib>GRÖNBERG, Henrik</creatorcontrib><creatorcontrib>WIKLUND, Fredrik</creatorcontrib><creatorcontrib>JIELIN SUN</creatorcontrib><creatorcontrib>LINDMARK, Fredrik</creatorcontrib><creatorcontrib>HSU, Fang-Chi</creatorcontrib><creatorcontrib>DIMITROV, Latchezar</creatorcontrib><creatorcontrib>BAOLI CHANG</creatorcontrib><creatorcontrib>TURNER, Aubrey R</creatorcontrib><creatorcontrib>WENNAN LIU</creatorcontrib><title>The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk</title><title>Cancer epidemiology, biomarkers & prevention</title><addtitle>Cancer Epidemiol Biomarkers Prev</addtitle><description>It is widely hypothesized that the interactions of multiple genes influence individual risk to prostate cancer. However, current
efforts at identifying prostate cancer risk genes primarily rely on single-gene approaches. In an attempt to fill this gap,
we carried out a study to explore the joint effect of multiple genes in the inflammation pathway on prostate cancer risk.
We studied 20 genes in the Toll-like receptor signaling pathway as well as several cytokines. For each of these genes, we
selected and genotyped haplotype-tagging single nucleotide polymorphisms (SNP) among 1,383 cases and 780 controls from the
CAPS ( CA ncer P rostate in S weden) study population. A total of 57 SNPs were included in the final analysis. A data mining method, multifactor dimensionality
reduction, was used to explore the interaction effects of SNPs on prostate cancer risk. Interaction effects were assessed
for all possible n SNP combinations, where n = 2, 3, or 4. For each n SNP combination, the model providing lowest prediction error among 100 cross-validations was chosen. The statistical significance
levels of the best models in each n SNP combination were determined using permutation tests. A four-SNP interaction (one SNP each from IL-10, IL-1RN, TIRAP , and TLR5 ) had the lowest prediction error (43.28%, P = 0.019). Our ability to analyze a large number of SNPs in a large sample size is one of the first efforts in exploring the
effect of high-order gene-gene interactions on prostate cancer risk, and this is an important contribution to this new and
quickly evolving field.</description><subject>association</subject><subject>Biological and medical sciences</subject><subject>Case-Control Studies</subject><subject>data mining</subject><subject>Genetic Predisposition to Disease</subject><subject>Genotype</subject><subject>Haplotypes</subject><subject>Humans</subject><subject>Inflammation</subject><subject>Male</subject><subject>MDR</subject><subject>Medical sciences</subject><subject>Medicin och hälsovetenskap</subject><subject>Nephrology. Urinary tract diseases</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Prognosis</subject><subject>prostate cancer</subject><subject>Prostatic Neoplasms - etiology</subject><subject>Prostatic Neoplasms - genetics</subject><subject>Prostatic Neoplasms - immunology</subject><subject>Registries - statistics & numerical data</subject><subject>Risk Factors</subject><subject>Signal Transduction</subject><subject>SNPs</subject><subject>Toll-Like Receptors - genetics</subject><subject>Tumors</subject><subject>Tumors of the urinary system</subject><subject>Urinary tract. Prostate gland</subject><issn>1055-9965</issn><issn>1538-7755</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkl9v0zAUxSMEYmPwEUB-AQmJDDu24_hxKtuoNIkKBq-W61w3ZvlTbEdVvz1Om9GniSdfWb9zfa_PybK3BF8SwqvPBHOeS1nyy-vVMsc8x5SXz7JzwmmVC8H581Q_MmfZqxB-Y4yF5PxldkbKomJUyPMM7htAyz6C1ya6oUeDRTfD6NEt9BCQ61E8ALbVXacPxErHZqf36Ifb9M46o_vY7tHKQ-1MDKkYQtQR0EL3Bjz67sLD6-yF1W2AN_N5kf28ub5ffM3vvt0uF1d3ueEFj7mBkpPKQi1kZWpKLRbUEEHWVjJrCEt7W4MxMyWjGgQTgmrMtV2LkjFSEHqR5ce-YQfbca223nXa79WgnZqvHlIFiouKVBMvnuS3fqhPokchkZwVvEjKT08qv7hfV2rwGzV2oyJMYJrwD0c8df0zQoiqc8FA2-oehjGoskrOSML-CxJJRSnLKoH8CJr038GD_TcCwWpKiJrcV5P7KiVEYa6mhCTdu_mBcd1BfVLNkUjA-xnQwejW-mSjCydOFIJSOjX6eOQat2l2zoMyB8M9BNDeNGnzNIcq0pv0L5321BY</recordid><startdate>20051101</startdate><enddate>20051101</enddate><creator>JIANFENG XU</creator><creator>LOWEY, James</creator><creator>ADAMI, Hans-Olov</creator><creator>SUH, Edward</creator><creator>MOORE, Jason H</creator><creator>ZHENG, S. Lilly</creator><creator>ISAACS, William B</creator><creator>TRENT, Jeffrey M</creator><creator>GRÖNBERG, Henrik</creator><creator>WIKLUND, Fredrik</creator><creator>JIELIN SUN</creator><creator>LINDMARK, Fredrik</creator><creator>HSU, Fang-Chi</creator><creator>DIMITROV, Latchezar</creator><creator>BAOLI CHANG</creator><creator>TURNER, Aubrey R</creator><creator>WENNAN LIU</creator><general>American Association for Cancer Research</general><scope>IQODW</scope><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>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D93</scope></search><sort><creationdate>20051101</creationdate><title>The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk</title><author>JIANFENG XU ; LOWEY, James ; ADAMI, Hans-Olov ; SUH, Edward ; MOORE, Jason H ; ZHENG, S. Lilly ; ISAACS, William B ; TRENT, Jeffrey M ; GRÖNBERG, Henrik ; WIKLUND, Fredrik ; JIELIN SUN ; LINDMARK, Fredrik ; HSU, Fang-Chi ; DIMITROV, Latchezar ; BAOLI CHANG ; TURNER, Aubrey R ; WENNAN LIU</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c525t-ce6518fed798cd33f073c171bf94fc14158fc004c643ae74773a05afb76441213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>association</topic><topic>Biological and medical sciences</topic><topic>Case-Control Studies</topic><topic>data mining</topic><topic>Genetic Predisposition to Disease</topic><topic>Genotype</topic><topic>Haplotypes</topic><topic>Humans</topic><topic>Inflammation</topic><topic>Male</topic><topic>MDR</topic><topic>Medical sciences</topic><topic>Medicin och hälsovetenskap</topic><topic>Nephrology. Urinary tract diseases</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Prognosis</topic><topic>prostate cancer</topic><topic>Prostatic Neoplasms - etiology</topic><topic>Prostatic Neoplasms - genetics</topic><topic>Prostatic Neoplasms - immunology</topic><topic>Registries - statistics & numerical data</topic><topic>Risk Factors</topic><topic>Signal Transduction</topic><topic>SNPs</topic><topic>Toll-Like Receptors - genetics</topic><topic>Tumors</topic><topic>Tumors of the urinary system</topic><topic>Urinary tract. Prostate gland</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>JIANFENG XU</creatorcontrib><creatorcontrib>LOWEY, James</creatorcontrib><creatorcontrib>ADAMI, Hans-Olov</creatorcontrib><creatorcontrib>SUH, Edward</creatorcontrib><creatorcontrib>MOORE, Jason H</creatorcontrib><creatorcontrib>ZHENG, S. Lilly</creatorcontrib><creatorcontrib>ISAACS, William B</creatorcontrib><creatorcontrib>TRENT, Jeffrey M</creatorcontrib><creatorcontrib>GRÖNBERG, Henrik</creatorcontrib><creatorcontrib>WIKLUND, Fredrik</creatorcontrib><creatorcontrib>JIELIN SUN</creatorcontrib><creatorcontrib>LINDMARK, Fredrik</creatorcontrib><creatorcontrib>HSU, Fang-Chi</creatorcontrib><creatorcontrib>DIMITROV, Latchezar</creatorcontrib><creatorcontrib>BAOLI CHANG</creatorcontrib><creatorcontrib>TURNER, Aubrey R</creatorcontrib><creatorcontrib>WENNAN LIU</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Umeå universitet</collection><jtitle>Cancer epidemiology, biomarkers & prevention</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>JIANFENG XU</au><au>LOWEY, James</au><au>ADAMI, Hans-Olov</au><au>SUH, Edward</au><au>MOORE, Jason H</au><au>ZHENG, S. Lilly</au><au>ISAACS, William B</au><au>TRENT, Jeffrey M</au><au>GRÖNBERG, Henrik</au><au>WIKLUND, Fredrik</au><au>JIELIN SUN</au><au>LINDMARK, Fredrik</au><au>HSU, Fang-Chi</au><au>DIMITROV, Latchezar</au><au>BAOLI CHANG</au><au>TURNER, Aubrey R</au><au>WENNAN LIU</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk</atitle><jtitle>Cancer epidemiology, biomarkers & prevention</jtitle><addtitle>Cancer Epidemiol Biomarkers Prev</addtitle><date>2005-11-01</date><risdate>2005</risdate><volume>14</volume><issue>11</issue><spage>2563</spage><epage>2568</epage><pages>2563-2568</pages><issn>1055-9965</issn><eissn>1538-7755</eissn><abstract>It is widely hypothesized that the interactions of multiple genes influence individual risk to prostate cancer. However, current
efforts at identifying prostate cancer risk genes primarily rely on single-gene approaches. In an attempt to fill this gap,
we carried out a study to explore the joint effect of multiple genes in the inflammation pathway on prostate cancer risk.
We studied 20 genes in the Toll-like receptor signaling pathway as well as several cytokines. For each of these genes, we
selected and genotyped haplotype-tagging single nucleotide polymorphisms (SNP) among 1,383 cases and 780 controls from the
CAPS ( CA ncer P rostate in S weden) study population. A total of 57 SNPs were included in the final analysis. A data mining method, multifactor dimensionality
reduction, was used to explore the interaction effects of SNPs on prostate cancer risk. Interaction effects were assessed
for all possible n SNP combinations, where n = 2, 3, or 4. For each n SNP combination, the model providing lowest prediction error among 100 cross-validations was chosen. The statistical significance
levels of the best models in each n SNP combination were determined using permutation tests. A four-SNP interaction (one SNP each from IL-10, IL-1RN, TIRAP , and TLR5 ) had the lowest prediction error (43.28%, P = 0.019). Our ability to analyze a large number of SNPs in a large sample size is one of the first efforts in exploring the
effect of high-order gene-gene interactions on prostate cancer risk, and this is an important contribution to this new and
quickly evolving field.</abstract><cop>Philadelphia, PA</cop><pub>American Association for Cancer Research</pub><pmid>16284379</pmid><doi>10.1158/1055-9965.EPI-05-0356</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1055-9965 |
| ispartof | Cancer epidemiology, biomarkers & prevention, 2005-11, Vol.14 (11), p.2563-2568 |
| issn | 1055-9965 1538-7755 |
| language | eng |
| recordid | cdi_swepub_primary_oai_swepub_ki_se_578181 |
| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; American Association for Cancer Research |
| subjects | association Biological and medical sciences Case-Control Studies data mining Genetic Predisposition to Disease Genotype Haplotypes Humans Inflammation Male MDR Medical sciences Medicin och hälsovetenskap Nephrology. Urinary tract diseases Polymorphism, Single Nucleotide Prognosis prostate cancer Prostatic Neoplasms - etiology Prostatic Neoplasms - genetics Prostatic Neoplasms - immunology Registries - statistics & numerical data Risk Factors Signal Transduction SNPs Toll-Like Receptors - genetics Tumors Tumors of the urinary system Urinary tract. Prostate gland |
| title | The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T01%3A50%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_swepu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Interaction%20of%20Four%20Genes%20in%20the%20Inflammation%20Pathway%20Significantly%20Predicts%20Prostate%20Cancer%20Risk&rft.jtitle=Cancer%20epidemiology,%20biomarkers%20&%20prevention&rft.au=JIANFENG%20XU&rft.date=2005-11-01&rft.volume=14&rft.issue=11&rft.spage=2563&rft.epage=2568&rft.pages=2563-2568&rft.issn=1055-9965&rft.eissn=1538-7755&rft_id=info:doi/10.1158/1055-9965.EPI-05-0356&rft_dat=%3Cproquest_swepu%3E68795914%3C/proquest_swepu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=19376968&rft_id=info:pmid/16284379&rfr_iscdi=true |