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Article(id=1149780468205318994, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149780466032669506, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2403106, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1714060800000, receivedDateStr=2024-04-26, revisedDate=1735747200000, revisedDateStr=2025-01-02, acceptedDate=null, acceptedDateStr=null, onlineDate=1752058625508, onlineDateStr=2025-07-09, pubDate=1744041600000, pubDateStr=2025-04-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752058625508, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752058625508, creator=13701087609, updateTime=1752058625508, updator=13701087609, issue=Issue{id=1149780466032669506, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='10', pageStart='3969', pageEnd='4395', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752058624990, creator=13701087609, updateTime=1768456644259, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218558743898411553, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149780466032669506, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218558743898411554, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149780466032669506, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4027, endPage=4036, ext={EN=ArticleExt(id=1149780468436005717, articleId=1149780468205318994, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Association Analysis and Prediction of Ferroptosis-Related Human Diseases and Genes, columnId=1156262732384031076, journalTitle=Science Technology and Engineering, columnName=Papers·Medicine, runingTitle=null, highlight=null, articleAbstract=

In order to reveal the association patterns between ferroptosis-related diseases and genes and predict potential Disease-Gene associations, ferroptosis-related research literature was analyzed to extract disease and gene entities, and a disease-gene complex network was constructed. The network's basic characteristics were further analyzed, and the Apriori algorithm was applied to extract strong disease-gene association rules. Link prediction technology was used to identify potential disease-gene associations. The results show as follows. ferroptosis plays a critical role in lethal diseases such as hepatocellular carcinoma, adenocarcinoma, breast cancer, and colorectal cancer. The genes such as GPX4 and ROS play key roles in cell survival or death through the regulation of iron homeostasis, oxidative stress, and lipid peroxidation. GPX4 and ROS are significantly associated with various diseases. The link prediction method revealed potential target genes for adenocarcinoma, lung cancer, colorectal cancer, and breast cancer, and preliminary validation of some predicted results was conducted through literature review.It is concluded that the research methodology employed in this study is both feasible and effective. The findings offer valuable references and suggest future directions for research on the prevention and treatment of ferroptosis-related diseases.

, correspAuthors=Yang-fang TAI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yang-fang TAI, Ying FU), CN=ArticleExt(id=1149780479173423993, articleId=1149780468205318994, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=铁死亡相关人类疾病与基因的关联分析与预测, columnId=1156262732526637414, journalTitle=科学技术与工程, columnName=论文·医药、卫生, runingTitle=null, highlight=null, articleAbstract=

为揭示铁死亡相关疾病与基因之间的关联规律、预测潜在的疾病-基因关联,通过分析铁死亡相关研究文献,提取了疾病和基因实体,构建疾病-基因复杂网络,进一步分析了网络的基本特征。采用Apriori算法提取疾病-基因的强关联规则,并通过链路预测技术识别潜在的疾病-基因关联对。结果表明:铁死亡在肝细胞癌、腺癌、乳腺癌、结直肠癌等致死性疾病中发挥了重要作用;GPX4、ROS等基因通过调控铁稳态、氧化应激和脂质过氧化等生物过程,在细胞生存或凋亡过程中具有关键性作用;GPX4、ROS等基因与多种疾病存在显著的关联性。链路预测技术揭示了腺癌、肺癌、结直肠癌、乳腺癌等癌症的潜在靶基因,并通过文献调研对部分预测结果进行了初步验证。可见,本文方法具有可行性和有效性。研究结果可为铁死亡相关疾病的预防与治疗研究提供有价值参考和未来方向。

, correspAuthors=邰杨芳, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=GoHoVlW0fy6S4FDvHpvtLw==, magXml=XGg7fiRfEVwjw7/8cIWgNg==, pdfUrl=null, pdf=bvAXQ/e3k2KL1LC7Wl3cHw==, pdfFileSize=12097338, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=YawXkCIuj6LqlrJjFH+3wA==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=jq8tkoyHLWDu2smeoj9SEg==, mapNumber=null, authorCompany=null, fund=null, authors=

邰杨芳(1974—),女,汉族,山西太原人,博士,副教授。研究方向:医学知识组织、知识挖掘、语义检索。E-mail:。

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邰杨芳(1974—),女,汉族,山西太原人,博士,副教授。研究方向:医学知识组织、知识挖掘、语义检索。E-mail:。

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邰杨芳(1974—),女,汉族,山西太原人,博士,副教授。研究方向:医学知识组织、知识挖掘、语义检索。E-mail:。

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NFATc4在不同恶性黑色素瘤亚型中的表达与功能研究[J]. 解放军医学院学报, 2024, 45(8): 891-897., articleTitle=NFATc4在不同恶性黑色素瘤亚型中的表达与功能研究, refAbstract=null), Reference(id=1218525110617686756, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, doi=null, pmid=null, pmcid=null, year=2024, volume=45, issue=8, pageStart=891, pageEnd=897, url=null, language=null, rfNumber=[34], rfOrder=52, authorNames=Rao Wei, Feng Jingyi, Wang Yilin, journalName=Academic Journal of Pla Postgraduate Medical School, refType=null, unstructuredReference=Rao Wei, Feng Jingyi, Wang Yilin, et al. Exploration of the function and mechanism of NFATc4 in malignant melanoma with diffe-rent subtypes[J]. Academic Journal of Pla Postgraduate Medical School, 2024, 45(8): 891-897., articleTitle=Exploration of the function and mechanism of NFATc4 in malignant melanoma with diffe-rent subtypes, refAbstract=null), Reference(id=1218525110751904496, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, doi=null, pmid=null, pmcid=null, year=2022, volume=40, issue=4, pageStart=365, pageEnd=378, url=null, language=null, rfNumber=[35], rfOrder=53, authorNames=Liao P, Wang W, Wang W, journalName=Cancer Cell, refType=null, unstructuredReference=Liao P, Wang W, Wang W, et al. CD8+ T cellsand fatty acids orchestrate tumor ferroptosis and immunity via ACSL4[J]. Cancer Cell, 2022, 40(4): 365-378., articleTitle=CD8+ T cellsand fatty acids orchestrate tumor ferroptosis and immunity via ACSL4, refAbstract=null)], funds=[Fund(id=1218525103512536010, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, awardId=J20230538, language=CN, fundingSource=山西省高等学校一般性教学改革创新项目(J20230538), fundOrder=null, country=null), Fund(id=1218525103671919577, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, awardId=J20240522, language=CN, fundingSource=山西省高等学校一般性教学改革创新项目(J20240522), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1218525098676503125, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, xref=null, ext=[AuthorCompanyExt(id=1218525098693280342, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, companyId=1218525098676503125, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Management, Shanxi Medical University, Taiyuan 030001, China), AuthorCompanyExt(id=1218525098701668951, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, companyId=1218525098676503125, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=山西医科大学管理学院, 太原 030001)])], figs=[ArticleFig(id=1218525101222445821, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=EN, label=Fig.1, caption=Researchidea map, figureFileSmall=OBh3kkyJGELuye3q7z6cuw==, figureFileBig=wcVIEKk5/PG81TxvX4H71g==, tableContent=null), ArticleFig(id=1218525101302137603, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=CN, label=图1, caption=研究思路图, figureFileSmall=OBh3kkyJGELuye3q7z6cuw==, figureFileBig=wcVIEKk5/PG81TxvX4H71g==, tableContent=null), ArticleFig(id=1218525101398606604, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=EN, label=Fig.2, caption=Disease-Gene association network map, figureFileSmall=ZsuZzNh9iNp+XTjUEyP4Xg==, figureFileBig=UyYRYL62wKqDiXydEPfyvw==, tableContent=null), ArticleFig(id=1218525101495075603, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=CN, label=图2, caption=疾病-基因关联网络图谱, figureFileSmall=ZsuZzNh9iNp+XTjUEyP4Xg==, figureFileBig=UyYRYL62wKqDiXydEPfyvw==, tableContent=null), ArticleFig(id=1218525101650264863, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=EN, label=Fig.3, caption=AUC scores for each algorithm for thedisease-gene co-occurrence matrix, figureFileSmall=WAf/bqdl3WkQ0nDQQQ69Jg==, figureFileBig=+RZoi8kr+v64/kwVxzMqZQ==, tableContent=null), ArticleFig(id=1218525101771899685, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=CN, label=图3, caption=疾病-基因共现矩阵的各算法AUC评分, figureFileSmall=WAf/bqdl3WkQ0nDQQQ69Jg==, figureFileBig=+RZoi8kr+v64/kwVxzMqZQ==, tableContent=null), ArticleFig(id=1218525101897728817, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=EN, label=Table 1, caption=

Network density analysis results

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 数值
平均度 19.913
网络直径 4
图密度 0.038
), ArticleFig(id=1218525102057112381, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=CN, label=表1, caption=

网络密度分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标 数值
平均度 19.913
网络直径 4
图密度 0.038
), ArticleFig(id=1218525102166164294, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=EN, label=Table 2, caption=

Centrality ranking results of disease degree

, figureFileSmall=null, figureFileBig=null, tableContent=
排名 疾病 度数
1 肝细胞癌(liver cell carcinomas) 227
2 腺癌(tubular carcinomas) 213
3 乳腺肿瘤(breast neoplasm) 173
4 结直肠癌(carcinomas, colorectal) 169
5 肺癌(cancers, lung) 162
6 鳞状细胞癌(carcinoma, planocellular) 145
7 急性肾损伤(kidney failures, acute) 142
8 恶性黑色素瘤(malignant melanoma) 132
9 胰腺癌(cancers, pancreas) 120
10 胃癌(cancers, gastric) 119
), ArticleFig(id=1218525102300382034, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=CN, label=表2, caption=

疾病度数中心性排名结果

, figureFileSmall=null, figureFileBig=null, tableContent=
排名 疾病 度数
1 肝细胞癌(liver cell carcinomas) 227
2 腺癌(tubular carcinomas) 213
3 乳腺肿瘤(breast neoplasm) 173
4 结直肠癌(carcinomas, colorectal) 169
5 肺癌(cancers, lung) 162
6 鳞状细胞癌(carcinoma, planocellular) 145
7 急性肾损伤(kidney failures, acute) 142
8 恶性黑色素瘤(malignant melanoma) 132
9 胰腺癌(cancers, pancreas) 120
10 胃癌(cancers, gastric) 119
), ArticleFig(id=1218525102568817502, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=EN, label=Table 3, caption=

Centrality ranking results of gene degree

, figureFileSmall=null, figureFileBig=null, tableContent=
排名 基因 度数
1 GPX4 98
2 ROS 97
3 SLC7A11 85
4 NRF2 79
5 ACSL4 70
6 HMOX1 63
7 TFRC 63
8 P53 59
9 FTH 55
10 FRG 51
), ArticleFig(id=1218525102703035247, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=CN, label=表3, caption=

基因度数中心性排名结果

, figureFileSmall=null, figureFileBig=null, tableContent=
排名 基因 度数
1 GPX4 98
2 ROS 97
3 SLC7A11 85
4 NRF2 79
5 ACSL4 70
6 HMOX1 63
7 TFRC 63
8 P53 59
9 FTH 55
10 FRG 51
), ArticleFig(id=1218525102833058684, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=EN, label=Table 4, caption=

List of disease-gene association rules

, figureFileSmall=null, figureFileBig=null, tableContent=
编号 疾病 基因 支持度 置信度 作用度
1 脑缺血(cerebral ischemia) GPX4 0.023 0.129 1.950
2 大脑中动脉闭塞(Middle cerebral artery occlusions) GPX4 0.014 0.114 1.730
3 急性肺损伤(lung injury,acute) GPX4 0.017 0.113 1.716
4 三阴性乳腺癌(triple negative breast neoplasms) GPX4 0.010 0.107 1.626
5 缺血性心肌病(ischemic heart disease) GPX4 0.014 0.094 1.426
6 非小细胞肺癌(carcinomas, non-small-cell lung) GPX4 0.017 0.090 1.368
7 急性肾损伤(kidney failures, acute) GPX4 0.029 0.087 1.319
8 肝纤维化(hepatic cirrhosis) GPX4 0.015 0.077 1.165
9 乳腺肿瘤(breast neoplasm) GPX4 0.040 0.074 1.122
10 非小细胞肺癌(carcinomas,non-small-cell lung) ROS 0.017 0.107 2.019
11 三阴性乳腺癌(triple negative breast neoplasms) ROS 0.010 0.102 1.918
12 乳腺肿瘤(breast neoplasm) ROS 0.040 0.088 1.666
13 白血病(leucocythaemia) ROS 0.017 0.087 1.635
14 胃癌(cancers, gastric) ROS 0.023 0.085 1.595
15 肺癌(cancers, lung) ROS 0.033 0.080 1.515
16 结直肠癌(carcinomas, colorectal) ROS 0.034 0.070 1.330
17 肝纤维化(hepatic cirrhosis) ROS 0.015 0.066 1.244
18 脑缺血(cerebral ischemia) ROS 0.023 0.064 1.214
19 急性肺损伤(lunginjury, acute) SLC7A11 0.017 0.061 1.742
20 卵巢癌(neoplasms, ovary) SLC7A11 0.020 0.054 1.534
21 脑缺血(cerebral ischemia) FGFR1 0.023 0.072 18.974
22 急性肺损伤(lung injury, acute) NRF2 0.017 0.065 3.090
), ArticleFig(id=1218525102971470728, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=CN, label=表4, caption=

疾病与基因的关联规则列表

, figureFileSmall=null, figureFileBig=null, tableContent=
编号 疾病 基因 支持度 置信度 作用度
1 脑缺血(cerebral ischemia) GPX4 0.023 0.129 1.950
2 大脑中动脉闭塞(Middle cerebral artery occlusions) GPX4 0.014 0.114 1.730
3 急性肺损伤(lung injury,acute) GPX4 0.017 0.113 1.716
4 三阴性乳腺癌(triple negative breast neoplasms) GPX4 0.010 0.107 1.626
5 缺血性心肌病(ischemic heart disease) GPX4 0.014 0.094 1.426
6 非小细胞肺癌(carcinomas, non-small-cell lung) GPX4 0.017 0.090 1.368
7 急性肾损伤(kidney failures, acute) GPX4 0.029 0.087 1.319
8 肝纤维化(hepatic cirrhosis) GPX4 0.015 0.077 1.165
9 乳腺肿瘤(breast neoplasm) GPX4 0.040 0.074 1.122
10 非小细胞肺癌(carcinomas,non-small-cell lung) ROS 0.017 0.107 2.019
11 三阴性乳腺癌(triple negative breast neoplasms) ROS 0.010 0.102 1.918
12 乳腺肿瘤(breast neoplasm) ROS 0.040 0.088 1.666
13 白血病(leucocythaemia) ROS 0.017 0.087 1.635
14 胃癌(cancers, gastric) ROS 0.023 0.085 1.595
15 肺癌(cancers, lung) ROS 0.033 0.080 1.515
16 结直肠癌(carcinomas, colorectal) ROS 0.034 0.070 1.330
17 肝纤维化(hepatic cirrhosis) ROS 0.015 0.066 1.244
18 脑缺血(cerebral ischemia) ROS 0.023 0.064 1.214
19 急性肺损伤(lunginjury, acute) SLC7A11 0.017 0.061 1.742
20 卵巢癌(neoplasms, ovary) SLC7A11 0.020 0.054 1.534
21 脑缺血(cerebral ischemia) FGFR1 0.023 0.072 18.974
22 急性肺损伤(lung injury, acute) NRF2 0.017 0.065 3.090
), ArticleFig(id=1218525103151825816, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=EN, label=Table 5, caption=

Disease-gene association combinations and unweighted PA algorithm values

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 疾病实体 基因
实体		 不加权
PA值
1 腺癌(tubular carcinomas) IL-6 14 670
2 腺癌(tubular carcinomas) SAS 12 388
3 腺癌(tubular carcinomas) MAPK8 9 780
4 腺癌(tubular carcinomas) LPS 9 454
5 腺癌(tubular carcinomas) HIF1 8 150
6 腺癌(tubular carcinomas) COX2 8 150
7 腺癌(tubular carcinomas) P62 8 150
8 肺癌(cancers, lung) TNF 11 960
9 肺癌(cancers, lung) GPX1 9 890
10 结直肠癌(carcinomas, colorectal) PMN E 10 845
11 结直肠癌(carcinomas, colorectal) SAS 9 158
12 乳腺癌(breast neoplasm) p38 8 448
13 乳腺癌(breast neoplasm) HMGB1 8 192
14 肝细胞癌(liver cell carcinomas) IL-6 15 570
15 肝细胞癌(liver cell carcinomas) TLR4 7 958
16 白血病(leucocythaemia) ACSL4 12 684
17 肾癌(nephroid carcinomas) NRF2 14 065
18 恶性黑色素瘤(malignant melanoma) COX2 9 231
19 胃癌(cancers, gastric) TNF 8 424
20 胰腺癌(cancers, pancreas) TNF 8 476
), ArticleFig(id=1218525103302820776, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149780468205318994, language=CN, label=表5, caption=

疾病-基因关联组合及不加权PA算法值

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 疾病实体 基因
实体		 不加权
PA值
1 腺癌(tubular carcinomas) IL-6 14 670
2 腺癌(tubular carcinomas) SAS 12 388
3 腺癌(tubular carcinomas) MAPK8 9 780
4 腺癌(tubular carcinomas) LPS 9 454
5 腺癌(tubular carcinomas) HIF1 8 150
6 腺癌(tubular carcinomas) COX2 8 150
7 腺癌(tubular carcinomas) P62 8 150
8 肺癌(cancers, lung) TNF 11 960
9 肺癌(cancers, lung) GPX1 9 890
10 结直肠癌(carcinomas, colorectal) PMN E 10 845
11 结直肠癌(carcinomas, colorectal) SAS 9 158
12 乳腺癌(breast neoplasm) p38 8 448
13 乳腺癌(breast neoplasm) HMGB1 8 192
14 肝细胞癌(liver cell carcinomas) IL-6 15 570
15 肝细胞癌(liver cell carcinomas) TLR4 7 958
16 白血病(leucocythaemia) ACSL4 12 684
17 肾癌(nephroid carcinomas) NRF2 14 065
18 恶性黑色素瘤(malignant melanoma) COX2 9 231
19 胃癌(cancers, gastric) TNF 8 424
20 胰腺癌(cancers, pancreas) TNF 8 476
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铁死亡相关人类疾病与基因的关联分析与预测
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邰杨芳 , 付颖
科学技术与工程 | 论文·医药、卫生 2025,25(10): 4027-4036
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科学技术与工程 | 论文·医药、卫生 2025, 25(10): 4027-4036
铁死亡相关人类疾病与基因的关联分析与预测
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邰杨芳 , 付颖
作者信息
  • 山西医科大学管理学院, 太原 030001

    • 邰杨芳(1974—),女,汉族,山西太原人,博士,副教授。研究方向:医学知识组织、知识挖掘、语义检索。E-mail:。

Association Analysis and Prediction of Ferroptosis-Related Human Diseases and Genes
Yang-fang TAI , Ying FU
Affiliations
  • School of Management, Shanxi Medical University, Taiyuan 030001, China
出版时间: 2025-04-08 doi: 10.12404/j.issn.1671-1815.2403106
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摘要
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为揭示铁死亡相关疾病与基因之间的关联规律、预测潜在的疾病-基因关联,通过分析铁死亡相关研究文献,提取了疾病和基因实体,构建疾病-基因复杂网络,进一步分析了网络的基本特征。采用Apriori算法提取疾病-基因的强关联规则,并通过链路预测技术识别潜在的疾病-基因关联对。结果表明:铁死亡在肝细胞癌、腺癌、乳腺癌、结直肠癌等致死性疾病中发挥了重要作用;GPX4、ROS等基因通过调控铁稳态、氧化应激和脂质过氧化等生物过程,在细胞生存或凋亡过程中具有关键性作用;GPX4、ROS等基因与多种疾病存在显著的关联性。链路预测技术揭示了腺癌、肺癌、结直肠癌、乳腺癌等癌症的潜在靶基因,并通过文献调研对部分预测结果进行了初步验证。可见,本文方法具有可行性和有效性。研究结果可为铁死亡相关疾病的预防与治疗研究提供有价值参考和未来方向。

铁死亡  /  关联网络  /  Apriori算法  /  关联规则  /  链路预测

In order to reveal the association patterns between ferroptosis-related diseases and genes and predict potential Disease-Gene associations, ferroptosis-related research literature was analyzed to extract disease and gene entities, and a disease-gene complex network was constructed. The network's basic characteristics were further analyzed, and the Apriori algorithm was applied to extract strong disease-gene association rules. Link prediction technology was used to identify potential disease-gene associations. The results show as follows. ferroptosis plays a critical role in lethal diseases such as hepatocellular carcinoma, adenocarcinoma, breast cancer, and colorectal cancer. The genes such as GPX4 and ROS play key roles in cell survival or death through the regulation of iron homeostasis, oxidative stress, and lipid peroxidation. GPX4 and ROS are significantly associated with various diseases. The link prediction method revealed potential target genes for adenocarcinoma, lung cancer, colorectal cancer, and breast cancer, and preliminary validation of some predicted results was conducted through literature review.It is concluded that the research methodology employed in this study is both feasible and effective. The findings offer valuable references and suggest future directions for research on the prevention and treatment of ferroptosis-related diseases.

ferroptosis  /  association network  /  apriori algorithm  /  association rules  /  link prediction
邰杨芳, 付颖. 铁死亡相关人类疾病与基因的关联分析与预测. 科学技术与工程, 2025 , 25 (10) : 4027 -4036 . DOI: 10.12404/j.issn.1671-1815.2403106
Yang-fang TAI, Ying FU. Association Analysis and Prediction of Ferroptosis-Related Human Diseases and Genes[J]. Science Technology and Engineering, 2025 , 25 (10) : 4027 -4036 . DOI: 10.12404/j.issn.1671-1815.2403106
正文
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在生命科学领域中,细胞死亡是维持组织稳定和平衡的关键过程。“铁死亡”是近年来被新发现的一种细胞死亡方式,其发生是由铁依赖性的脂质过氧化和活性氧(reactive oxygen species, ROS)的积累所驱动[1],已被证实在多种病理过程中发挥着重要作用。近年来,关于铁死亡在特定疾病如卵巢癌[2]、心血管疾病[3]、中枢神经系统疾病[4]中的作用机制,研究者们取得了重要进展。例如,铁死亡的抑制在防治神经退行性疾病如帕金森病和阿尔茨海默病中显示出显著效果,而其激活则为靶向治疗肿瘤细胞提供了新的策略。这些研究为探索更具针对性的治疗策略提供了重要的理论基础。
然而,已有研究主要关注铁死亡相关特定疾病与基因之间的关联,但对其整体关联的研究仍相对缺乏,这限制了针对性治疗策略的发展。本文研究旨在通过文本挖掘和生物信息学分析,系统梳理铁死亡相关的文献,揭示铁死亡相关疾病与基因之间的关联规律、预测疾病潜在的关联基因,为疾病的预防和治疗提供新的视角。
1 铁死亡相关研究现状
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自2012年铁死亡概念首次被提出后[1],这种新型细胞死亡方式已引起了广泛关注与研究。在调控机制方面,李彩菲等[2]揭示了铁死亡在卵巢癌中的具体调控机制,重点探讨了谷胱甘肽过氧化酶4(glutathione peroxidase 4, GPX4)活性及ROS产生在该过程中的关键作用,为卵巢癌的预防和治疗提供了新的视角。在疾病关联研究方面,刘浩等[5]系统概述了铁死亡与中枢神经系统疾病的关联,揭示了铁死亡在此疾病发生发展中的作用,为相关疾病的治疗提供了科学依据。在遗传学研究方面,张博文等[6]聚焦于铁死亡的表观遗传调控机制,发现了组蛋白修饰和非编码RNA在调控铁死亡中的重要作用。在药物研究方面,魏阳阳等[7]探讨了铁死亡在脑出血中的作用及其药物治疗的研究进展,为脑出血后继发性脑损伤的治疗提供了新的思路。
上述分析可见,现有研究在铁死亡调控机制及其与疾病治疗方面取得了一定进展,且主要聚焦在某单一疾病或基因的研究上。生物医学领域中,铁死亡相关疾病与基因之间关联的整体情况如何,目前尚未有研究对其进行系统性梳理与总结。为此,本文研究通过分析生物医学领域所有铁死亡相关研究文献,深入探讨铁死亡在不同疾病中的生物学过程,识别与之关联的关键基因,并预测潜在的疾病-基因关联,旨在为未来的疾病预防和治疗研究提供有价值的参考。
2 研究思路与方法
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2.1 研究思路
本文研究旨在分析与预测铁死亡相关的人类疾病-基因之间的关联,主要包括以下3个步骤。首先,获取数据并进行预处理。即收集和整理铁死亡领域的相关文献,筛选符合标准的数据。其次,通过命名实体识别技术识别和提取摘要文本中的疾病-基因实体,构建疾病-基因共现矩阵。最后,进行关联网络及链路预测分析。利用可视化软件和Apriori算法揭示相关疾病、基因之间的关联程度;运用链路预测技术预测未来铁死亡相关疾病与基因之间的潜在关系。研究思路图如图1所示。
2.2 数据处理
2.2.1 数据获取与预处理
考虑到数据的代表性、权威性及规范性,选取PubMed平台作为数据来源,检索式为:检索式1:((((Ferroptosis[MeSH Terms]) OR (ferroptosis[Title/Abstract])) OR (ferroptotic cell death[Title/Abstract])) OR (Oxytosis[Title/Abstract])) NOT (review[Publication Type]);检索式2:((((Iron overload[Title/Abstract]) OR (iron accumulation[Title/Abstract])) OR (iron metabolism[Title/Abstract])) AND (cell death[Title/Abstract])) NOT (review[Publication Type]),得到除综述之外的文献7 292篇,检索时间为2023年8月6日。人工筛选后去除无关文献,得到最终文献6 463篇。
2.2.2 实体抽取
对于6 463篇文献的摘要文本,采用自编Java程序进行基于词典方法的疾病、基因的实体抽取。在实体词典中,同一实体概念的不同名称编号相同。对得到的实体基于编号进行合并处理后得到379种疾病实体和3 776种基因实体。
2.2.3 疾病-基因关联矩阵构建
基于疾病和基因两类实体在同一篇摘要文本中的共现关系,构建疾病-基因关联关系矩阵。矩阵的行表示基因,列表示疾病,矩阵中的元素代表两个实体在同一篇文章中的共现次数,共现次数的大小表明两个实体之间关联关系的紧密程度。
2.3 研究方法
2.3.1 社会网络分析
社会网络分析是一种研究系统中相互关联和相互作用的网络结构的方法。网络中通常包含大量的节点和节点之间的连接[8]。为了解疾病和基因的关联关系,分别采用Gephi和VOSviewer软件,对共现网络的整体特征进行社会网络分析,并进行聚类可视化。
2.3.2 关联规则分析
关联规则分析是一种在大规模数据集中寻找频繁项集并推导出项集之间关联规则的方法[9]。本文研究采用Apriori算法,对共现数据进行关联规则分析,挖掘出满足一定支持度和置信度条件下频繁出现的疾病-基因关联对。
2.3.3 链路预测
链路预测是指如何通过已知的网络节点以及网络结构等信息,预测网络中尚未产生连边的两个节点之间产生连接的可能性[10]。本文研究从11种相似性指标中选择效果评价指标曲线下AUC(area under the curve)最大的一种,作为预测未来铁死亡相关疾病与基因之间的潜在关系的方法。
3 结果与分析
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3.1 铁死亡相关疾病与基因的关联网络特征分析
为了清晰地展示疾病-基因关联网络的整体结构,筛选频次≥5的疾病和基因实体,采用VOSviewer对疾病和基因的关系网络进行可视化的展示,如图2所示。
3.1.1 网络整体特征分析
图2由553个节点(99个疾病实体节点,454个基因实体节点)和5 558条边构成。
运用Gephi工具运行计算得到铁死亡相关疾病与基因关联网络的密度分析结果如表1所示。节点的平均度为19.913,表明网络中节点间的连接性较强,每个疾病或基因都平均与其他20个基因或疾病有直接关联。网络直径为4,表明网络结构紧凑,信息传递路径短,疾病与基因之间的关联通过最多4个中介节点即可实现,有利于疾病与基因之间关联的快速传递和发现。网络图密度为0.038,表示网络中实际存在的连接仅占可能存在的最大边数的3.80%,意味着虽然存在一些高度边接的重要节点,但整体上节点之间的连接较少,说明网络中存在特定的疾病-基因关联模式或集群,但大部分节点之间没有直接关联。
3.1.2 网络节点特征分析
节点的中心度指标反映节点在网络中的地位和作用的大小。本文研究以度数中心度大小为依据,排名前10的疾病和基因节点的网络特征信息分别如表2和表3所示。
排名前10的疾病节点包括肝细胞癌(liver cell carcinomas)、腺癌(tubular carcinomas)、乳腺癌(breast neoplasm)、结直肠癌(carcinomas, colorectal)、肺癌(cancers,lung)、鳞状细胞癌(carcinoma, plan-ocellular)、急性肾损伤(kidnSSey failures,acute)、恶性黑色素瘤(malignant melanoma)、胰腺癌(cancers,pancreas)、胃癌(cancers, gastric)。这10种疾病中许多都是常见的致死性疾病,如肺癌、乳腺癌和胃癌等,且涵盖了多个器官系统,表明铁死亡在不同器官中发挥重要作用。研究这些疾病与铁死亡的关系有助于深入了解这些疾病的发病机制,为疾病的预防和治疗提供新的策略。
排名前10的基因节点包括GPX4、ROS、SLC7A11、NRF2、ACSL4、HMOX1、TFRC、P53、FTH、FRG。这10个基因都是铁死亡过程中的关键调控因子,它们通过不同的机制影响了铁稳态、氧化应激和脂质过氧化等过程。进一步研究这些基因在铁死亡中的作用机制,将有助于深入理解铁死亡的生物学过程,并为肿瘤等疾病的防治提供新的思路和方法。
3.2 铁死亡相关疾病与基因的关联分析
3.2.1 疾病与基因数据的关联规则挖掘
采用Apriori算法对数据进行关联规则挖掘。设定最小支持度为0.01,最小置信度为0.05,提升度大于1,最终提取出23条强关联规则,如表4所示。
3.2.2 疾病-基因的关联规则分析
1)关联对:疾病与GPX4
关联规则1~9揭示了脑缺血、大脑中动脉闭塞、急性肺损伤、三阴性乳腺癌、缺血性心肌病、非小细胞肺癌、急性肾损伤、肝纤维化、乳腺肿瘤等九种疾病均与基因存在强关联关系。GPX4是参与脂质过氧化过程的抑制蛋白,能够降解小分子过氧化物以及相对复杂的脂质过氧化物。GPX4的过表达或敲降能够影响细胞对铁死亡诱导剂的敏感性[2],其在维持细胞膜完整性和对抗过氧化物毒性方面发挥着关键作用。
具体而言,通过调节 GPX4,能够减轻脑缺血以及脑缺血再灌注损伤引发的神经功能障碍等不良后果[11]。在急性肺损伤中,GPX4活性的降低会加剧细胞膜和组织的损伤,进而加重疾病的发展[12]。在三阴性乳腺癌中,GPX4的表达水平降低,导致铁死亡过程被激活,从而影响癌症的进展[13]。在缺血性心肌病中,冠状动脉血流受限导致心肌缺血,GPX4的活性受到影响,导致对氧化应激的抵抗力下降[14]。
2) 关联对:疾病与ROS
表4中的关联规则10~规则18揭示了乳腺肿瘤、非小细胞肺癌、三阴性乳腺癌、白血病、胃癌、肺癌、结直肠癌、肝纤维化、脑缺血9种疾病均与ROS基因存在强关联关系,强调了ROS的产生及随后的·OH(羟基自由基)介导的脂质过氧化是最终导致铁死亡的核心事件。
在非小细胞肺癌,ROS的异常升高导致DNA损伤、基因突变和细胞增殖异常,从而促进疾病的发生,并通过信号通路的激活和转录因子的调节等机制,影响疾病的进展[15]。对于乳腺癌,特别是三阴性乳腺癌,调节ROS的平衡可以影响肿瘤的生长和转移[16]。在白血病中,某些分子突变导致ROS水平升高,增加了白血病细胞对铁死亡的易感性,这一机制中的关键分子可作为潜在的治疗靶点[17]。
3)关联对:疾病与其他基因
关联规则19和规则20揭示了急性肺损伤和卵巢癌与SLC7A11基因存在强关联关系。有研究表明STAT6可通过调控SLC7A11蛋白抑制铁死亡,从而减轻急性肺损伤[18]。
关联规则21揭示了脑缺血与FGFR1基因存在强关联关系。研究表明FGFR1信号通路的激活可以在某些脑缺血模型中发挥保护作用,有助于减轻缺血导致的脑损伤[19]。
关联规则22~规则23揭示了急性肺损伤与NRF2、LPS基因存在强关联关系。NRF2通过调控铁死亡相关的通路,影响肺损伤的发生与进展,而LPS则通过诱导炎症反应加剧肺损伤[20]。
3.3 铁死亡相关疾病与基因的关联预测
3.3.1 预测指标的选取
链路预测的研究方法主要有3种:基于相似性的链路预测、基于似然分析的链路预测和基于机器学习的链路预测[10]。为了更好地预测铁死亡相关人类疾病与基因潜在关系,本文研究通过基于相似性的多项指标分析含权和不含权网络,并选择最佳指标来计算未链接节点之间的连接可能性。
应用上述算法测算两两节点间的相似度,每对节点被赋予一个分数值。不同算法计算结果不同,为此还需评估算法的测度准确性。本文研究通过Python实现链路预测算法,并计算了AUC指标,算法输入为共现矩阵,算法输出为基于各种算法计算得到的节点间相似度矩阵以及每一个算法的准确度AUC。
图3展示了11种加权和不加权算法(AA、CN、HDI、HPI、jaccard、LHN、lp、PA、RA、Salton、Sorenson)在铁死亡相关人类疾病与基因潜在关系预测中的AUC得分。由图3可见,不加权的优先连接(preferential attachment index,PA)算法预测潜在关系时表现最佳。因此,选择此算法来发现铁死亡相关人类疾病与基因的潜在链接。PA指标描述了网络增长过程中新节点或新连接更倾向于依附到已具有较高度数的节点上的偏好性原则,高PA值的节点对更有可能在未来形成连接,成为研究的新焦点,其不加权算法计算公式[27]为
S ( x , y ) = k x k y
式(1)中: S x y为节点 x和节点 y之间的优先连接指数; k x和 k y分别为节点 x和 y的度数。
3.3.2 疾病与基因的关联预测
采用不加权PA算法预测未来铁死亡相关疾病与基因之间的潜在关系,PA值排名靠前的20个潜在“疾病-基因”关联对如表5所示。
3.3.3 疾病-基因关联预测结果分析
对表3中的疾病-基因的潜在关联对进行分析,发现关联对中的疾病实体均为癌症,并且癌症基因关联对呈现出一对多、二对多、二对一和一对一的关系。
1)癌症与基因:一对多关联
IL-6、SAS、MAPK8、LPS、HIF1和COX2基因在未来可能与腺癌有潜在的链接机会。
第一,IL-6作为一种重要的炎症细胞因子,能够调控调节性T细胞和T辅助17细胞(Th17)的产生,从而引发肿瘤微环境中的炎症反应,这表明IL-6可能通过影响免疫系统来影响腺癌的发展[21]。第二,SAS作为铁死亡的诱导剂,肿瘤细胞可能对其产生耐药性,这表明铁死亡在肿瘤细胞的生存与死亡之间发挥着重要的作用[22]。第三,MAPK8是氧化应激中发挥关键作用的信号通路之一,而铁死亡正是由细胞膜中含多不饱和脂肪酸的磷脂的过氧化所驱动。因此,MAPK8通路可能影响腺癌的发展与进程[23]。第四,LPS作为内毒素,可以通过激活免疫反应和释放炎症细胞因子来引发肺部炎症反应,这表明LPS可能通过引发的炎症反应来影响腺癌的发展[20]。第五,HIF1作为SLC1A1上游转录因子,可以激活其表达,促进谷氨酸摄取,进而交换更多细胞外胱氨酸,最终导致铁死亡[24]。第六,COX2是铁死亡标志蛋白之一,主要表达于肺泡上皮细胞和巨噬细胞中,这表明COX2可能通过影响肺泡上皮细胞和巨噬细胞的活性来影响腺癌的发展[25]。第七,P62是一种多功能蛋白,其可能通过调节细胞内氧化还原平衡、自噬以及免疫反应等机制影响腺癌的发生和发展[26]。
2)癌症与基因:一对二关联
TNF和GPX1基因在未来可能与肺癌有潜在的链接机会。TNF通过促进谷胱甘肽的生物合成,保护细胞免受铁死亡的影响,其抑制剂与铁死亡诱导剂的联合使用在类风湿性关节炎治疗中可能具有潜在的疗效[27]。相反,GPX1作为铁死亡过程中关键的抗氧化酶,对调控细胞的抗氧化能力至关重要。这为探索新的肺癌治疗策略提供了潜在靶点[28]。
PMN E和SAS基因在未来可能与结直肠癌有潜在的链接机会。PMN E是一种中性粒细胞中的酶,在肿瘤微环境中可导致铁死亡,从而促进肿瘤的生长[29]。而SAS在唾液酸合成过程中起着关键作用,这可能改变肿瘤细胞的黏附性和迁移能力,从而影响结直肠癌的发展[22]。
p38和HMGB1基因在未来可能与乳腺癌有潜在的链接机会。p38信号通路的激活可以通过促进铁死亡来抑制乳腺癌细胞的生存和增殖[30]。而HMGB1可以调节肝纤维化中的铁死亡过程[31]。
IL-6和TLR4基因在未来可能与肝细胞癌有潜在的链接机会。IL-6可以迅速被激活并释放到局部组织,随后通过血液循环迅速传播到全身各个部位,这可以调节免疫反应[32]。而抑制TLR4的表达功能则可以减少铁死亡相关蛋白的表达,从而抑制氧化应激和线粒体损伤过程[33]。
3)癌症与基因:一对一关联
COX2基因在未来可能与恶性黑色素瘤有潜在的链接机会。COX2是一种与炎症和癌症有关的酶,可能通过催化前列腺素的合成来调节ROS水平,其在黑色素瘤中高表达,与肿瘤的发展和转移有关[34]。
NRF2基因在未来可能与肾癌有潜在的链接机会。NRF2是抗氧化作用的关键转录因子,可以调控多个与铁死亡相关的基因。ACSL4基因在未来可能与白血病有潜在的链接机会,该基因通过催化多不饱和脂肪酸的辅酶A衍生物的合成,参与调节细胞膜磷脂的合成和脂质组成,从而影响铁死亡的过程[35]。
4)癌症与基因:二对一关联
胰腺癌和胃癌则未来则会与TNF基因相关,该基因可以促进胱氨酸摄取和谷胱甘肽的生物合成,从而保护细胞免受铁死亡的侵袭[27]。
未来在研究癌症时,需要进一步探讨这些基因在铁死亡中的作用和机制,以便为癌症治疗提供新的思路和方法。
4 结论
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基于对铁死亡相关研究文献中疾病与基因数据的分析与挖掘,得出如下结论。
(1)铁死亡在肝细胞癌、腺癌、乳腺癌、结直肠癌等常见致死性疾病中具有关键作用。GPX4、ROS、SLC7A11、NRF2、ACSL4等基因在铁死亡过程中的调控作用涉及铁稳态、氧化应激和脂质过氧化等生物过程,对于细胞的生存或凋亡过程至关重要。
(2)GPX4、ROS等关键基因与多种疾病之间的强关联关系,这一发现将为疾病的预防和治疗提供新的视角和途径。
(3)通过预测算法得到了腺癌、肺癌、结直肠癌、乳腺癌等癌症的潜在靶基因并进行了初步论证,预测结果为疾病的未来研究提供了新的方向和线索。
本文研究的不足之处在于,所得结论来源于对科研文献数据分析与挖掘,下一步研究可考虑纳入更具证据性的临床试验、疾病、基因等数据库数据,并且对研究结果进行生物医学相关实验的进一步验证。
基金
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  • 山西省高等学校一般性教学改革创新项目(J20230538)
  • 山西省高等学校一般性教学改革创新项目(J20240522)
文献
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2025年第25卷第10期
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doi: 10.12404/j.issn.1671-1815.2403106
  • 接收时间：2024-04-26
  • 首发时间：2025-07-09
  • 出版时间：2025-04-08
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  • 收稿日期：2024-04-26
  • 修回日期：2025-01-02
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山西省高等学校一般性教学改革创新项目(J20230538)
山西省高等学校一般性教学改革创新项目(J20240522)
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    山西医科大学管理学院, 太原 030001
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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