Additional file 3 of Predicting response to immunotherapy in gastric cancer via assessing perineural invasion-mediated inflammation in tumor microenvironment

Additional file 3 of Predicting response to immunotherapy in gastric cancer via assessing perineural invasion-mediated inflammation in tumor microenvironment

Additional file 3: Table S1. Clinical and pathologic characteristics of patients with gastric cancer and pan-gastrointestinal tumor. Table S2. 200 genes and their functions of MSigdb Hallmarks inflammatory response. Table S3. 22 genes from MCODE. Table S4. Univariate Cox regreesion of 200 genes with...

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Hauptverfasser: Li, Xunjun, Wang, Yiyun, Zhai, ZhongYa, Mao, Qingyi, Chen, Dianjie, Xiao, Luxi, Xu, Shuai, Wu, Qilin, Chen, Keming, Hou, Qiantong, He, Qinglie, Shen, Yuyang, Yang, Manchun, Peng, Zishan, He, Siqing, Zhou, Xuanhui, Tan, Haoyang, Luo, Shengwei, Fang, Chuanfa, Li, Guoxin, Chen, Tao
Format: Dataset
Sprache:eng
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FOS: Clinical medicine
Immunology
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Zusammenfassung:Additional file 3: Table S1. Clinical and pathologic characteristics of patients with gastric cancer and pan-gastrointestinal tumor. Table S2. 200 genes and their functions of MSigdb Hallmarks inflammatory response. Table S3. 22 genes from MCODE. Table S4. Univariate Cox regreesion of 200 genes with PNI and No PNI. Table S5. High NII score VS Low NII score. Table S6. Go analysis of No PNI and NII.clusterB. Table S7. Description of 47 genes used to construct NII score. Table S8. Multivariable Cox regression analysis of variables of nomogram in training cohort and whole cohort. Table S9. NII.score. Table S10. Subgroup.
DOI:10.6084/m9.figshare.23930456