MEMORY uncovers the enrichment of mitosis and immune signatures in multiple cancers. (A) 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.

Identification of hub genes in mitosis or immune-related cancers. (A) The SAS of all GEARs was calculated, and CSN was constructed. (B) 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. (C-D) The CSN of LUAD and BRCA. The enlarged section represents the hub gene. (E) LUAD clustering based on hub gene expression, compared with inhibitor-based classification52. (F-G) LUAD clustering based on hub gene expression in comparison with the classic classification.

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, ERBB2 mutations, and ALK, ROS1 fusions in three LUAD subgroups. (C) Kaplan-Meyer overall survival curves for EGFR-mutation samples of LUAD by hub gene subgroups. (D) Kaplan-Meyer 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), ML vs MH (E).

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 red; 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. (C) The heat map showed the hub gene expression of A549 after compounds treatment from cMAP database98. Blue annotations meant 76 compounds that can inhibit hub gene expression. (D) Comparison of the IC50 z-score of five tumor cell growth inhibitors for A549 (KRAS mutation) and SW1573 (KRAS and PIK3CA mutation).

The hub gene classification of BRCA revealed a significant association between EMT and immune infiltration. (A) The hub gene expression heatmap containing the result of hub gene classification and PAM50 classification of BRCA58. (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.

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 was analyzed. (B) The correlation of immune cell infiltration rate of 33 TCGA cancer types with CDH1 expression was analyzed. (C) The mitosis and immune-related pathway scores of 33 cancer types were analyzed, and the 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.