Multi-gradient permutation survival analysis identifies mitosis and immune signatures steadily associated with cancer patient prognosis
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, China
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, China
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, China
- The Second Affiliated Hospital, Zhejiang University School of Medicine, China
- School of Life Science and Technology, ShanghaiTech University, China
- Department of Pathology and Frontier Innovation Center, School of Basic Medical Sciences, Fudan University, China
Figures
Figure 1 with 2 supplements
MEMORY uncovers the enrichment of mitosis and immune signatures in multiple cancers.
(A) Algorithm of MEMORY. Sample sizes, ranging from 10% to 100% with 10% intervals (‘gradient’), were used for 1000 permutations of survival analyses of all 15 cancer types. Each matrix was divided into high and low expression groups based on median gene expression values, and survival analyses were performed. Log-rank test significance results were coded as 1 for significant (p<0.05) and 0 for non-significant (p>0.05) outcomes, forming survival analysis matrices and the summarized significant probability matrix, which allowed the identification of GEARs. (B) The maximum of significant probability for each gradient sample size of every cancer type. Sample number rate refers to the percentage of samples in each sampling gradient compared to the total number of samples. (C) The pathways were enriched by GOEA based on the GEARs of each cancer type. The displayed pathways represent the top 5 most significant pathways for each cancer type. Mitosis-related pathways were marked in red, whereas immune-related pathways were marked in blue. Only pathways with a Benjamini–Hochberg–adjusted p<0.05 are displayed.
Figure 1—figure supplement 1
Correlation between sample size and the number of prognostic genes identified by MEMORY across 15 cancer types.
(A–O) The number of genes significantly related to prognosis which were obtained through MEMORY was positively correlated with sample size. The vertical value of the dotted line is 0.8.
Figure 1—figure supplement 2
The accumulation patterns of significant probability for GEARs across sampling gradients.
(A–O) The significant probability of GEAR at each gradient. The vertical value of the dotted line is 0.8.
Figure 2 with 4 supplements
Identification of hub genes in mitosis or immune-related cancers.
(A) GEARs and core survival networks. The SAS of all GEARs was calculated, and CSN was constructed. (B) Cancers and their hub gene pathways. The left nodes represent five different cancers, while the right nodes detail the functional types of hub-gene pathways. The height of the edges in the middle represents the proportion of hub-gene pathways corresponding to a specified functional type. Only pathways with a Benjamini–Hochberg–adjusted p<0.05 are displayed. (C–D) The CSN of LUAD and BRCA. The enlarged section represents the hub gene. (E) Heat map showing the hub gene expression of A549 after compounds treatment from cMAP database (Subramanian et al., 2017). Blue annotations meant 76 compounds that can inhibit hub gene expression. (F–G) LUAD clustering based on hub gene expression in comparison with the classic classification.
Figure 2—figure supplement 1
CSN and identification of hub genes in 15 cancer types.
(A–O) The core survival networks of 15 cancer types. Red nodes are hub genes.
Figure 2—figure supplement 2
Hierarchical clustering of 15 cancer types based on hub gene expression.
(A–O) 15 cancer types were genotyped through hierarchical clustering based on the expression of hub genes, and the samples of each cancer were divided into three clusters.
Figure 2—figure supplement 3
Association between hub gene-based molecular subtypes and clinical stages across pan-cancer.
(A) The proportion of hub gene classifications contained in different stages of pan-cancer level. (B–K) The proportion of cancers at different stages contained in different hub gene classifications.
Figure 2—figure supplement 4
Gene dependency, drug sensitivity screening, and prognostic validation of hub gene subgroups in LUAD.
(A–C) The gene dependency analysis of hub genes for LGG (A), LIHC (B), LUAD (C). (D) Relative sensitivity of A549 cell line to all compounds from the GDSC database, calculated based on IC50 values. (E) The number of compounds associated with A549 in the cMAP and GDSC databases. (F) Kaplan-Meier overall survival curves for LUAD classic subgroups. (G) Kaplan-Meier overall survival curves for LUAD hub gene subgroups. (H) Kaplan-Meier overall survival curves for KRAS-mutation samples of LUAD by hub gene subgroups. (I) Kaplan-Meier overall survival curves for BRAF-mutation samples of LUAD by hub gene subgroups.
Figure 3
Different LUAD subgroups were characterized by unique genetic mutation profiles.
(A) TMB analysis in three groups of LUAD samples. ***p<0.0005. (B) Proportion of different oncogenic drivers, including KRAS, EGFR, BRAF, and ERBB2 mutations, and ALK and ROS1 fusions in three LUAD subgroups. (C) Kaplan-Meier overall survival curves for EGFR-mutation samples of LUAD by hub gene subgroups. (D) Kaplan-Meier overall survival curves for pan-negative LUAD samples by hub gene subgroups. (E–G) Comparison of top gene mutations in three LUAD clusters, including ML vs. MM (C), MM vs. MH (D), and ML vs MH (E).
Figure 4
PIK3CA mutation associates with mitosis and drug resistance in LUAD.
(A) Cancer-related gene mutations were annotated based on gene dependency scores data from Depmap database. Functional mutations were indicated in green; functional (subtype-associated) mutations were highlighted in orange; non-functional mutations were indicated in gray. (B) The analysis of the NeST differential pathways across three groups including ML vs. MM, MM vs. MH, ML vs MH. Only pathways with a Benjamini–Hochberg–adjusted p<0.05 are displayed. (C) Comparison of the IC50 z-score of five tumor cell growth inhibitors for A549 (KRAS mutation) and SW1573 (KRAS and PIK3CA mutation).
Figure 5 with 2 supplements
The hub gene classification of BRCA revealed a significant association between EMT and immune infiltration.
(A) Hub gene expression heatmap. The heatmap contains the result of hub gene classification and PAM50 classification of BRCA (Thorsson et al., 2018). (B) Comparison of molecular classification based on hub genes with PAM50 classification. (C–E) Comparison of gene mutations in three groups of BRCA samples, including IL vs. IM (C), IM vs. IH (D), IL vs. IH (E). (F) Comparison of the total immune cell rate in three BRCA clusters. ***p<0.0005. (G) Comparison of CDH1 expression in BRCA samples with wildtype or mutant CDH1. ***p<0.0005. (H) A schematic diagram illustrating the correlation between the EMT score and immune cell rate. The red line represents the fitting curve. The correlation analysis was performed using Pearson correlation coefficient. Statistical significance for all pairwise group comparisons was assessed with the Wilcoxon test.
Figure 5—figure supplement 1
Prognostic analysis of BRCA molecular subtypes based on hub gene signatures.
(A) Kaplan-Meier overall survival curves for BRCA classic subgroups. (B) Kaplan-Meier overall survival curves for BRCA PAM50 subgroups. (C–F) Kaplan-Meier overall survival curves for BRCA samples by hub gene subgroups in LumA (C), LumB (D), Basal (E), Her2 (F).
Figure 5—figure supplement 2
Immune cell infiltration ratio and their correlation with CDH1-mediated EMT in BRCA.
(A–J) Comparison of infiltration of different types of immune cells in three group BRCA samples. ***p<0.0005, **p<0.005 and *p<0.05. (K) Comparison of the proportion of immune cells in the total cells in BRCA samples with high CDH1 expression and low CDH1 expression. ***p<0.0005. (L) Schematic diagram of the correlation between CDH1 expression and VIM expression. The red line was a fitting curve. Pearson correlation coefficient was used for correlation analysis. (M) Schematic diagram of the correlation between CDH1 expression and TWIST2 expression. The red line was a fitting curve. Pearson correlation coefficient was used for correlation analysis.
Figure 6
Mitosis and immune signatures predict patient prognosis at the pan-cancer level.
(A) The correlation of mitosis scores of 33 TCGA cancer types with PIK3CA expression. (B) The correlation of immune cell infiltration rate of 33 TCGA cancer types with CDH1 expression. (C) The mitosis and immune-related pathway scores of 33 cancer types. Median score was utilized as a threshold to categorize patients for survival analysis. Among the 33 cancer types examined, 10 cancer types were exclusively associated with the mitosis score, 2 cancer types were exclusively associated with the immune score, and 5 cancer types showed a correlation with both mitosis and immunity scores concurrently. Dots represent values from log-rank tests comparing high vs. low score groups for each cancer type. The dashed line marks the nominal significance threshold (P=0.05).
Author response image 1
The intersection of the network constructed by various number of edges.
Additional files
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MDAR checklist
- https://cdn.elifesciences.org/articles/101619/elife-101619-mdarchecklist1-v1.pdf
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Supplementary file 1
Supplementary tables containing dataset sampling sizes, GEARs, top 10 GEARs, core survival network, hub genes, sample classification, and mitotic signature.
- https://cdn.elifesciences.org/articles/101619/elife-101619-supp1-v1.xlsx
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Multi-gradient permutation survival analysis identifies mitosis and immune signatures steadily associated with cancer patient prognosis
eLife 13:RP101619.
https://doi.org/10.7554/eLife.101619.3
