{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T17:06:40Z","timestamp":1777482400269,"version":"3.51.4"},"reference-count":51,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancer Biotherapy and Radiopharmaceuticals"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>This study investigated potential gene targets and metabolite markers associated with colorectal carcinoma (CRC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials &amp; Methods:<\/jats:title>\n                    <jats:p>Gene expression data (GSE110224) related with CRC were obtained from Gene Expression Omnibus, including 17 tumor tissues and 17 normal colon ones. The gene differential analysis, functional analysis, protein\u2013protein interaction (PPI) analysis, and metabolite network construction were performed to identify key genes related to CRC. Moreover, an external dataset was used to validate genes of interest in CRC, and corresponding survival analysis was also conducted.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      The authors extracted 197 differentially expressed genes (75 upregulated and 122 downregulated genes). Moreover, upregulated genes were closely associated with rheumatoid arthritis and amoebiasis pathways. The downregulated genes were mainly related to bile secretion and proximal tubule bicarbonate reclamation pathway. Combined with PPI network and metabolite prediction, the overlapped nine genes (\n                      <jats:italic toggle=\"yes\">CXCL1, CXCL8, CXCL10, HDS1782, IL18, PCK1, PTGS2, SERPINB2, TMP1<\/jats:italic>\n                      ) were found to be critical in CRC. Similar gene expression profiles of nine critical genes were validated by an external dataset, except for\n                      <jats:italic toggle=\"yes\">SERPINB2<\/jats:italic>\n                      . In addition, the expressions of\n                      <jats:italic toggle=\"yes\">TIMP1, IL1B,<\/jats:italic>\n                      and\n                      <jats:italic toggle=\"yes\">PTGS2<\/jats:italic>\n                      were closely related with prognosis. Finally, the metabolite network analysis revealed that there were close associations between prostaglandin E2 and three pathways (rheumatoid arthritis, amoebiasis, and leishmaniasis).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>\n                      <jats:italic toggle=\"yes\">CXCL1<\/jats:italic>\n                      \/\n                      <jats:italic toggle=\"yes\">CXCL8\/IL1B\/PTGS2<\/jats:italic>\n                      -prostaglandin E2 axes were the potential signatures involved in CRC progression, which could provide new insights to understand the molecular mechanisms of CRC.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/cbr.2020.3980","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T17:48:04Z","timestamp":1606844884000},"page":"907-916","source":"Crossref","is-referenced-by-count":3,"title":["Integrated Analysis of Gene Expression and Metabolite Data Reveals Candidate Molecular Markers in Colorectal Carcinoma"],"prefix":"10.1177","volume":"37","author":[{"given":"Junsheng","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Oncology, Zaozhuang Municipal Hospital, Zaozhuang City, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shan","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Oncology, Zaozhuang Municipal Hospital, Zaozhuang City, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiqing","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Oncology, Zaozhuang Municipal Hospital, Zaozhuang City, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shifeng","family":"Kan","sequence":"additional","affiliation":[{"name":"Department of Oncology, Zaozhuang Municipal Hospital, Zaozhuang City, China."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21551"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21577"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21442"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-009-0881-y"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2019.09.046"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1111\/cas.14127"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13148-015-0128-7"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.3892\/ijo.2017.3843"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gene.2019.01.001"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-09695-9"},{"key":"e_1_3_4_12_2","first-page":"439","article-title":"Integrating proteomics and transcriptomics for the identification of potential targets in early colorectal cancer","volume":"55","author":"Yang W","year":"2019","unstructured":"Yang W, , Shi J, , Zhou Y, et al. 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