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Firstly, we selected and downloaded GSE15824, GSE50161, and GSE86574 from the GEO database, which included 40 LGG tissue and 28 normal brain tissue samples. GEO and VENN software identified of 206 codifference expressed genes (DEGs). Secondly, we applied the DAVID online software to investigate the DEG biological function and KEGG pathway enrichment, as well as to build the protein interaction visualization network through Cytoscape and STRING website. Then, the MCODE plug is used in the analysis of 22 core genes. Thirdly, the 22 core genes were analyzed with UNCLA software, of which 18 genes were associated with a worse prognosis. Fourthly, GEPIA was used to analyze the 18 selected genes, and 14 genes were found to be a significantly different expression between LGGs and normal brain tumor samples. Fifthly, hierarchical gene clustering was used to examine the 14 important gene expression differences in different histologies, as well as analysis of the KEGG pathway. Five of these genes were shown to be abundant in the natural killer cell-mediated cytokines (NKCC) and phagosome pathways. The five key genes that may be affected by the immune microenvironment play a crucial role in LGG development.","package_hash":"885e3da0c3b286766124897f775577ecfe05f6c384624c0eef8b3307b89dd0f1","plan_hash":null,"plan_version":null,"first_receipt_at":"2026-11-04T07:03:23+00:00","decided_at":null,"days_open":5,"intake":{"method":"sci_import","source_name":"Applied bionics and biomechanics","source_identifier":"pmid:35035531","source_url":null,"corpus_record_id":"pmid:35035531","recorded_by":"Journal Editor","ingested_at":"2026-11-04T07:03:23+00:00"},"lineage":{"relationship":"original","parent":null,"descendants":[]}},"source_text":{"held":false,"kind":"abstract","what_the_kind_means":"only the abstract was read","characters":null,"sha256":null,"intact":null,"note":"The hash is of the text as it arrived. 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\"activity-reading-sci-2026-11-01\",\n    \"type\": \"classification\",\n    \"used\": [\n     {\n      \"id\": \"pmid:35035531\",\n      \"type\": \"paper\",\n      \"label\": \"pmid:35035531\"\n     },\n     {\n      \"id\": \"source:pmid:35035531\",\n      \"type\": \"manuscript\",\n      \"label\": \"PMID 35035531 full text\"\n     },\n     {\n      \"id\": \"data:geo:GSE15824\",\n      \"type\": \"dataset\",\n      \"label\": \"GEO GSE15824\"\n     }\n    ],\n    \"agent\": \"agent:corpus-assay-engine\"\n   },\n   {\n    \"id\": \"activity-read-35035531\",\n    \"type\": \"extraction\",\n    \"used\": [\n     \"source:pmid:35035531\"\n    ],\n    \"agent\": \"agent:minimax-reader\"\n   },\n   {\n    \"id\": \"activity-resolve-GSE15824\",\n    \"type\": \"accession_resolution\",\n    \"used\": [\n     \"data:geo:GSE15824\"\n    ],\n    \"agent\": \"agent:corpus-assay-engine\"\n   }\n  ],\n  \"agents\": [\n   {\n    \"id\": \"agent:corpus-assay-engine\",\n    \"type\": \"software\",\n    \"label\": \"Corpus assay engine\"\n   },\n   {\n    \"id\": \"agent:minimax-reader\",\n    \"type\": \"software\",\n    \"label\": \"MiniMax literature reader\"\n   }\n  ],\n  \"source_run_id\": \"reading-sci-2026-11-01\",\n  \"dsl_catalogue_fingerprint\": \"343fb30756ba6ec3\",\n  \"capability_table_fingerprint\": \"2cb3e42f047d2ecf\",\n  \"snapshot_id\": \"ba34b49490d9bfaf\"\n },\n \"lineage\": {\n  \"parent_id\": \"pmid:35035531\",\n  \"parent_hash\": \"sha256:8be8f9216685fefd5e228b569040e0e135a5b0d43abc46f9e5a06243b0374a6c\",\n  \"fork_of\": null,\n  \"generation\": 2\n },\n \"gaps\": [\n  \"asset:data:geo:GSE15824 licence not read\",\n  \"asset:data:geo:GSE15824 not downloaded\",\n  \"asset:data:geo:GSE50161 licence not read\",\n  \"asset:data:geo:GSE50161 not downloaded\",\n  \"asset:data:geo:GSE86574 licence not read\",\n  \"asset:data:geo:GSE86574 not downloaded\",\n  \"execution.not_run\",\n  \"method.reader_named_unmapped\",\n  \"review.human_critique\",\n  \"review.independent_reader\",\n  \"review.machine_critique\"\n ],\n \"assays\": [\n  {\n   \"role\": \"analysis\",\n   \"definition_id\": \"clustering\",\n   \"definition_version\": \"f2132e96ae6c7a4e\",\n   \"definition_digest\": null,\n   \"required_operations\": [\n    \"hierarchical_cluster\"\n   ],\n   \"support_state\": \"supported\",\n   \"source_hashes\": {\n    \"dsl_catalogue\": \"f2132e96ae6c7a4e\"\n   },\n   \"confidence\": 0.8,\n   \"evidence\": [\n    \"hierarchical clustering\"\n   ],\n   \"order_basis\": \"analysis_only\",\n   \"id\": \"assay:1\"\n  }\n ]\n}\n","document":{"sci_version":"1.2","schema":"https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json","id":"pmid:35035531","kind":"science-record","revision":3,"content_hash":"sha256:851dadf7e8fb65db72442d9d878b6e871e13fda562d0fc561532a96a64a18e27","created_at":"2026-11-01T17:36:45Z","updated_at":"2026-11-01T21:26:59Z","generator":{"name":"corpus-assay-engine","run_id":"reading-sci-2026-11-01"},"paper":{"state":"complete","title":"Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis.","doi":"10.1155/2022/6959237","pmid":"35035531","authors":["Fangzhou Guo","Jun Yan","Guoyuan Ling","Hainan Chen","Qianrong Huang","Junbo Mu","Ligen Mo"],"journal":"Applied bionics and biomechanics","year":2022,"abstract":"Lower-grade glioma (LGG) is a common type of central nervous system tumor. Due to its complicated pathogenesis, the choice and timing of adjuvant therapy after tumor treatment are controversial. This study explored and identified potential therapeutic targets for lower-grade. The bioinformatics method was employed to identify potential biomarkers and LGG molecular mechanisms. Firstly, we selected and downloaded GSE15824, GSE50161, and GSE86574 from the GEO database, which included 40 LGG tissue and 28 normal brain tissue samples. GEO and VENN software identified of 206 codifference expressed genes (DEGs). Secondly, we applied the DAVID online software to investigate the DEG biological function and KEGG pathway enrichment, as well as to build the protein interaction visualization network through Cytoscape and STRING website. Then, the MCODE plug is used in the analysis of 22 core genes. Thirdly, the 22 core genes were analyzed with UNCLA software, of which 18 genes were associated with a worse prognosis. Fourthly, GEPIA was used to analyze the 18 selected genes, and 14 genes were found to be a significantly different expression between LGGs and normal brain tumor samples. Fifthly, hierarchical gene clustering was used to examine the 14 important gene expression differences in different histologies, as well as analysis of the KEGG pathway. Five of these genes were shown to be abundant in the natural killer cell-mediated cytokines (NKCC) and phagosome pathways. The five key genes that may be affected by the immune microenvironment play a crucial role in LGG development.","claims":[{"id":"claim-method","statement":"P value < 0.05 and logFC (fold change) > 2 were defined statistically. Subsequently, DEGs from the three datasets have been commonly integrated using the web tool Venn diagram (http://bioinformatics.psb.ugent.be/beg/tools/venn-diagrams) [12].","kind":"methodological","target":null,"evidence":[{"asset":"source:pmid:35035531","locator":"sections[4] \"2.2. Detection of DEGs\" paragraph 2","quote":"P value < 0.05 and logFC (fold change) > 2 were defined statistically. Subsequently, DEGs from the three datasets have been commonly integrated using the web tool Venn diagram (http://bioinformatics.psb.ugent.be/beg/tools/venn-diagrams) [12]."}],"state":"complete"},{"id":"claim-data-access","statement":"The data used to support and demonstrate the results of this investigation are available on request from the corresponding author.","kind":"methodological","target":null,"evidence":[{"asset":"source:pmid:35035531","locator":"sections[17] \"Data Availability\" paragraph 2","quote":"The data used to support and demonstrate the results of this investigation are available on request from the corresponding author."}],"state":"complete"},{"id":"claim-target-result","statement":"Analysis of three GEO microarray datasets identified 206 common differentially expressed genes in lower-grade glioma.","kind":"qualitative","target":null,"evidence":[{"asset":"source:pmid:35035531","locator":"sections[9] \"3.1. Detection of DEGs in LGGs\" paragraph 2","quote":"Following that, Venn diagram software was used to evaluate and detect the common genes between the three datasets."}],"state":"complete"}],"gaps":[]},"experiment":{"state":"complete","objective":"Analysis of three GEO microarray datasets identified 206 common differentially expressed genes in lower-grade glioma.","steps":[{"id":"read_published_estimate","order":1,"name":"Read the published estimate from the source","kind":"other","state":"complete","blocked_on":null,"description":"The claim, method and evidence span published with the source article.","duration_hours":null,"parameters":[],"inputs":["source:pmid:35035531"],"outputs":["the published estimate and its evidence"],"instruments":[],"reagents":[]},{"id":"obtain_source_data","order":2,"name":"Obtain the source data from the named repository","kind":"data-preparation","state":"complete","blocked_on":null,"description":"The GEO series accession resolves and its sample accessions are listed. 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