Table_1_Multi-Omics Characterization of a Glycerolipid Metabolism-Related Gene Enrichment Score in Colon Cancer.xlsx

BackgroundGlycerolipid metabolism is involved in the genesis and progression of colon cancer. The current study aims at exploring the prognostic value and potential molecular mechanism of glycerolipid metabolism-related genes in colon cancer from the perspective of multi-omics.MethodsClinical information and mRNA expression data of patients with colon cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Single-sample gene set enrichment analysis (ssGSEA) was applied to calculate the glycerolipid metabolism-related gene enrichment score (GLMS). Univariable and multivariable Cox regression analyses were used to study the prognostic value of GLMS in TCGA-COAD and GSE39582 cohorts. The molecular mechanism of the prognostic factor was investigated via immune cell infiltration estimation and correlation analysis of cancer hallmark pathways. Single-cell transcriptomic dataset GSE146771 was used to identify the cell populations which glycerolipid metabolism targeted on.ResultsThe GLMS was found to be associated with tumor location and consensus molecular types (CMSs) of colon cancer in TCGA-COAD cohort (P ConclusionWe demonstrated that GLMS was a potential independent prognostic factor for colon cancer. The GLMS was also correlated with several cancer hallmark pathways, as well as immune microenvironment.</p

sj-docx-1-tam-10.1177_17588359211038477 – Supplemental material for TGFBR2 mutation predicts resistance to immune checkpoint inhibitors in patients with non-small cell lung cancer

Supplemental material, sj-docx-1-tam-10.1177_17588359211038477 for TGFBR2 mutation predicts resistance to immune checkpoint inhibitors in patients with non-small cell lung cancer by Teng Li, Han Wang, Jiachen Xu, Chengcheng Li, Yudong Zhang, Guoqiang Wang, Yutao Liu, Shangli Cai, Wenfeng Fang, Junling Li and Zhijie Wang in Therapeutic Advances in Medical Oncology</p

Table1_DNA Damage Response Gene-Based Subtypes Associated With Clinical Outcomes in Early-Stage Lung Adenocarcinoma.DOCX

DNA damage response (DDR) pathways play a crucial role in lung cancer. In this retrospective analysis, we aimed to develop a prognostic model and molecular subtype based on the expression profiles of DDR-related genes in early-stage lung adenocarcinoma (LUAD). A total of 1,785 lung adenocarcinoma samples from one RNA-seq dataset of The Cancer Genome Atlas (TCGA) and six microarray datasets of Gene Expression Omnibus (GEO) were included in the analysis. In the TCGA dataset, a DNA damage response gene (DRG)–based signature consisting of 16 genes was constructed to predict the clinical outcomes of LUAD patients. Patients in the low-DRG score group had better outcomes and lower genomic instability. Then, the same 16 genes were used to develop DRG-based molecular subtypes in the TCGA dataset to stratify early-stage LUAD into two subtypes (DRG1 and DRG2) which had significant differences in clinical outcomes. The Kappa test showed good consistency between molecular subtype and DRG (K = 0.61, p < 0.001). The DRG subtypes were significantly associated with prognosis in the six GEO datasets (pooled estimates of hazard ratio, OS: 0.48 (0.41–0.57), p < 0.01; DFS: 0.50 (0.41–0.62), p < 0.01). Furthermore, patients in the DRG2 group benefited more from adjuvant therapy than standard-of-care, which was not observed in the DRG1 group. In summary, we constructed a DRG-based molecular subtype that had the potential to predict the prognosis of early-stage LUAD and guide the selection of adjuvant therapy for early-stage LUAD patients.</p

MOESM1 of Incidence rates of immune-related adverse events and their correlation with response in advanced solid tumours treated with NIVO or NIVO+IPI: a systematic review and meta-analysis

Additional file 1: Method. Search strategy. Table S1. Characteristics of the included studies. Table S2. Incidences of Categorical irAEs according to system organ class. Table S3. Sensitivity analysis of the correlation between irAEs and ORR in NIVO. Table S4. Sensitivity analysis of the correlation between irAEs and ORR in NIVO+IPI. Figure S1. Correlation between irAEs and ORR of chemotherapy-contained regimen. References (DOCX 276 kb

Additional file 1 of Deciphering a cell death-associated signature for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma

Supplementary Material

Additional file 2 of Deciphering a cell death-associated signature for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma

Supplementary Material

Additional file 2 of Identification and validation of tissue or ctDNA PTPRD phosphatase domain deleterious mutations as prognostic and predictive biomarkers for immune checkpoint inhibitors in non-squamous NSCLC

Additional file 2: Figure S1. PTPRD mutation was not a prognostic biomarker for ICIs in squamous lung cancers. a-c PFS of PTPRD mutation/WT group squamous lung cancer in validation cohort-1 (a), validation cohort-2 (b), MSKCC-240 cohort (c). d OS of PTPRD mutation/WT group squamous lung cancer in MSKCC-350 cohort. e-g PFS of PTPRD mutation/WT group squamous lung cancer in validation cohort-3 (e), OAK (f) and POPLAR (g) cohort. h-j OS of PTPRD mutation/WT group of squamous lung cancers in validation cohort-3 (h), OAK (i) and POPLAR (j) cohort. Figure S2. Predicting PFS efficiency of PTPRD mutation in 4 tissue cohorts. a-b PFS of PTPRD mutation/WT group of NSCLC (a) or ns-NSCLC (b) in Miao & N.Rizvi cohort. c-d PFS of PTPRD mutation/WT group NSCLC (c) or ns-NSCLC (d) in Hellmann cohort. e-f PFS of PTPRD mutation/WT group of NSCC (e) or ns-NSCLC (f) in MSKCC-240 cohort. Figure S3. Predicting OS efficiency of PTPRD mutation in 2 tissue cohorts. a-b Overall survivals of PTPRD mutation/WT group of NSCLC (a) or ns-NSCLC (b) in MSKCC-350. c-d Overall survivals of PTPRD mutation/WT group of NSCLC (c) or ns-NSCLC (d) in Miao cohort. Figure S4. Predicting prognosis efficiency of ctDNA PTPRD mutation in OAK/POPLAR cohort. a-b PFS of PTPRD mutation/WT group of NSCLC in OAK (a) or POPLAR (b) cohort. c-d PFS of PTPRD mutation/WT group of ns-NSCLC in OAK (c) or POPLAR (d) cohort. e-f OS of PTPRD mutation/WT group of NSCLC in OAK (e) or POPLAR (f) cohort. g-h OS of PTPRD mutation/WT group of ns-NSCLC in OAK (g) or POPLAR (h) cohort. Figure S5. Predicting prognosis efficiency of PTPRD other-mut. a-b PFS of phosphatase-mut (a) or WT (b) vs other-mut in ns-NSCLC of the discovery cohort. c-d PFS of phosphatase-mut (c) or WT (d) vs other-mut in ns-NSCLC of validation cohort-1. e-f OS of phosphatase-mut (e) or WT (f) vs other-mut in ns-NSCLC of validation cohort-2. g-h PFS of phosphatase-mut (g) or WT (h) vs other-mut in ns-NSCLC of a merged cohort of validation cohort-3. i-j OS of phosphatase-mut (i) or WT (j) vs other-mut in ns-NSCLC of validation cohort-3. Figure S6. Predicting prognosis efficiency of PTPRD different mutation types in tissue cohorts. a-c PFS of WT vs phosphatase-mut (a), other-mut vs WT (b) and phosphatase-mut vs other-mut (c) in ns-NSCLC in the MSKCC-240 cohort. d-f PFS of WT vs phosphatase-mut (d), other-mut vs WT (e) and phosphatase-mut vs other-mut (f) in ns-NSCLC in the Hellmann cohort. g-i PFS of WT vs phosphatase-mut (g), other-mut vs WT (h) and phosphatase-mut vs other-mut (i) in ns-NSCLC in the Miao & N.Rizvi cohort. j-l OS of WT vs phosphatase-mut (j), other-mut vs WT (k) and phosphatase-mut vs other-mut (l) in ns-NSCLC in the MSKCC-350 cohort. m-o OS of WT vs phosphatase-mut (m), other-mut vs WT (n) and phosphatase-mut vs other-mut (o) in ns-NSCLC in the Miao cohort. Figure S7. Predicting prognosis efficiency of different PTPRD mutation types in ctDNA cohorts.a-c PFS of WT vs phosphatase-mut (a), other-mut vs WT (b) and phosphatase-mut vs other-mut (c) in ns-NSCLC in the OAK cohort. d-f PFS of WT vs phosphatase-mut (d), other-mut vs WT (e) and phosphatase-mut vs other-mut (f) in ns-NSCLC in the POPLAR cohort. g-i OS of WT vs phosphatase-mut (g), other-mut vs WT (h) and phosphatase-mut vs other-mut (i) in ns-NSCLC in the OAK cohort. j-l OS of WT vs phosphatase-mut (j), other-mut vs WT (k) and phosphatase-mut vs other-mut (l) in ns-NSCLC in the POPLAR cohort. Figure S8. PTPRD phosphatase-mut predicting prognosis efficiency was independent on TP53/EGFR/KRAS mutation. a-d Interaction plot of PTPRD and TP53, EGFR, KRAS, STK11, KEAP1 in WES cohort (a), MSKCC-240 and MSKCC-350 cohort (b), OAK/POPLAR cohort (c) and TCGA-LUAD cohort (d). e-i HR forest plot of TP53/EGFR/KRAS in ns-NSCLC of discover cohort (e), validation cohort-1(f), validation cohort-2(g) and validation cohort-3 (h-i). Figure S9. PTPRD is not a prognosis biomarker for surgery and adjuvant chemotherapy in lung adenocarcinomas. a-b PFS of WT vs mutation (a) or phosphatase-mut (b) in TCGA-LUAD cohort. c PFS of PTPRD expression high vs low in TCGA-LUAD. d-e OS of WT vs mutation (d) or phosphatase-mut (e) in TCGA-LUAD cohort. f OS of PTPRD expression high vs low in TCGA-LUAD. Figure S10. PTPRD mutation predicting prognosis efficiency in other cancer types.a-f PTPRD mutations predicting OS efficiency in melanoma (a), bladder cancer (b), esophagastric cancer (c), head and neck cancer (d), colorectal cancer (e) and cancer of unknown primary (f) of NG1661 cohort

Additional file 1 of Identification and validation of tissue or ctDNA PTPRD phosphatase domain deleterious mutations as prognostic and predictive biomarkers for immune checkpoint inhibitors in non-squamous NSCLC

Additional file 1: Table S1. Patient characteristics in NCC cohort. Table S2. Details of 8 NSCLC cohorts analyzed in this study. Table S3. Prediction of each PTPRD mutations based on PROVEAN. Table S4. GSEA of TCGA-LUSC cohort

A 15-Inflammation-Related Gene Signature Predicts the Prognosis of Patients With Pancreatic Ductal Adenocarcinoma

Chronic inflammation promotes the development of pancreatic ductal adenocarcinoma (PDAC) and PDAC-related inflammatory tumor microenvironment facilitates tumor growth and metastasis. Thus, we aimed to study the association between inflammatory response and prognosis in patients with PDAC. We conducted the whole transcriptomic sequencing using tissue samples collected from patients diagnosed with PDAC (n = 106) recruited from Shandong Cancer Hospital. We first constructed a prognostic signature using 15 inflammation-related genes in The Cancer Genome Atlas (TCGA) cohort (n = 177) and further validated it in an independent International Cancer Genome Consortium (ICGC) cohort (n = 90) and our in-house cohort. PDAC patients with a higher risk score had poorer overall survival (OS) (P P P = 0.01; HR, 1.94; 95% CI, 1.14-3.30) and our cohort (P + T cells were higher in patients with a lower risk score (P P < 0.05). In sum, we identified a novel gene signature that was associated with inflammatory response for risk stratification, prognosis prediction, and therapy guidance in PDAC patients. Future studies are warranted to validate the clinical utility of the signature.</p

figure_3-only_online-4.11-revised – Supplemental material for An open-label randomised comparison of aripiprazole, olanzapine and risperidone for the acute treatment of first-episode schizophrenia: Eight-week outcomes

Supplemental material, figure_3-only_online-4.11-revised for An open-label randomised comparison of aripiprazole, olanzapine and risperidone for the acute treatment of first-episode schizophrenia: Eight-week outcomes by Zhang Cheng, Yanbo Yuan, Xue Han, Lei Yang, Shangli Cai, Fude Yang, Zheng Lu, Chuanyue Wang, Hong Deng, Jingping Zhao, Yutao Xiang, Christoph U Correll and Xin Yu in Journal of Psychopharmacology</p