Additional file 1 of Genetic and clinical landscape of ER + /PR- breast cancer in China

Additional file 1: Supplemental Table S1. Baseline characteristics of patients in Cohort 2

Additional file 10 of Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

Additional file 10: Figure S6. Network of GO enriched terms. (A) The color represents the GO terms. (B) The color represents the p-value. The more genes enriched in the terms, the darker the color. Go, Gene ontology

Image2_Analysis of Omics Data Reveals Nucleotide Excision Repair-Related Genes Signature in Highly-Grade Serous Ovarian Cancer to Predict Prognosis.JPEG

Most of the high-grade serous ovarian cancers (HGSOC) are accompanied by P53 mutations, which are related to the nucleotide excision repair (NER) pathway. This study aims to construct a risk signature based on NER-related genes that could effectively predict the prognosis for advanced patients with HGSOC. In our study, we found that two clusters of HGSOC with significantly different overall survival (OS) were identified by consensus clustering and principal component analysis (PCA). Then, a 7-gene risk signature (DDB2, POLR2D, CCNH, XPC, ERCC2, ERCC4, and RPA2) for OS prediction was developed subsequently based on TCGA cohort, and the risk score-based signature was identified as an independent prognostic indicator for HGSOC. According to the risk score, HGSOC patients were divided into high-risk group and low-risk group, in which the distinct OS and the predictive power were also successfully verified in the GEO validation sets. Then we constructed a nomogram, including the risk signature and clinical-related risk factors (age and treatment response) that predicted an individual’s risk of OS, which can be validated by assessing calibration curves. Furthermore, GSEA showed that the genes in the high-risk group were significantly enriched in cancer-related pathways, such as “MAPK signaling pathway”, “mTOR signaling pathway”, “VEGF signaling pathway” and so on. In conclusion, our study has developed a robust NER-related genes-based molecular signature for prognosis prediction, and the nomogram could be used as a convenient tool for OS evaluation and guidance of therapeutic strategies in advanced patients with HGSOC.</p

Image1_Analysis of Omics Data Reveals Nucleotide Excision Repair-Related Genes Signature in Highly-Grade Serous Ovarian Cancer to Predict Prognosis.JPEG

Most of the high-grade serous ovarian cancers (HGSOC) are accompanied by P53 mutations, which are related to the nucleotide excision repair (NER) pathway. This study aims to construct a risk signature based on NER-related genes that could effectively predict the prognosis for advanced patients with HGSOC. In our study, we found that two clusters of HGSOC with significantly different overall survival (OS) were identified by consensus clustering and principal component analysis (PCA). Then, a 7-gene risk signature (DDB2, POLR2D, CCNH, XPC, ERCC2, ERCC4, and RPA2) for OS prediction was developed subsequently based on TCGA cohort, and the risk score-based signature was identified as an independent prognostic indicator for HGSOC. According to the risk score, HGSOC patients were divided into high-risk group and low-risk group, in which the distinct OS and the predictive power were also successfully verified in the GEO validation sets. Then we constructed a nomogram, including the risk signature and clinical-related risk factors (age and treatment response) that predicted an individual’s risk of OS, which can be validated by assessing calibration curves. Furthermore, GSEA showed that the genes in the high-risk group were significantly enriched in cancer-related pathways, such as “MAPK signaling pathway”, “mTOR signaling pathway”, “VEGF signaling pathway” and so on. In conclusion, our study has developed a robust NER-related genes-based molecular signature for prognosis prediction, and the nomogram could be used as a convenient tool for OS evaluation and guidance of therapeutic strategies in advanced patients with HGSOC.</p

Additional file 6 of Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

Additional file 6: Figure S3. The type of gene alteration among 628 patietns/696 samples in 3 publicly datasets including TCGA from cBioportal. 23 (3%) patients have gene alternation

Additional file 3 of Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

Additional file 3: Table S3. 40 overlapping candidate genes

Additional file 7 of Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

Additional file 7: Figure S4. Time-dependent ROC analysis of the five prognosis-related genes in 1-, 3- and 5-year OS prediction. A C9orf135. B DPP6. C PLG. D ATP6V0D2. E PADI1. ROC, receiver operating characteristic

Additional file 4 of Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

Additional file 4: Figure S1. Kaplan–Meier survival analysis of nine prognosis-related genes used in BSR. BSR, best subset regression

Image3_Analysis of Omics Data Reveals Nucleotide Excision Repair-Related Genes Signature in Highly-Grade Serous Ovarian Cancer to Predict Prognosis.JPEG

Most of the high-grade serous ovarian cancers (HGSOC) are accompanied by P53 mutations, which are related to the nucleotide excision repair (NER) pathway. This study aims to construct a risk signature based on NER-related genes that could effectively predict the prognosis for advanced patients with HGSOC. In our study, we found that two clusters of HGSOC with significantly different overall survival (OS) were identified by consensus clustering and principal component analysis (PCA). Then, a 7-gene risk signature (DDB2, POLR2D, CCNH, XPC, ERCC2, ERCC4, and RPA2) for OS prediction was developed subsequently based on TCGA cohort, and the risk score-based signature was identified as an independent prognostic indicator for HGSOC. According to the risk score, HGSOC patients were divided into high-risk group and low-risk group, in which the distinct OS and the predictive power were also successfully verified in the GEO validation sets. Then we constructed a nomogram, including the risk signature and clinical-related risk factors (age and treatment response) that predicted an individual’s risk of OS, which can be validated by assessing calibration curves. Furthermore, GSEA showed that the genes in the high-risk group were significantly enriched in cancer-related pathways, such as “MAPK signaling pathway”, “mTOR signaling pathway”, “VEGF signaling pathway” and so on. In conclusion, our study has developed a robust NER-related genes-based molecular signature for prognosis prediction, and the nomogram could be used as a convenient tool for OS evaluation and guidance of therapeutic strategies in advanced patients with HGSOC.</p

Additional file 1 of Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma

Additional file 1: Table S1. Clinical information of TCGA KIRC cohort and GSE39609 cohort