Fig 1.
General protein-coding associated mutation status of CAL protein-coding genes in pan-cancer.
(A) The waterfall plot, (B) variant classification, (C) variant type and (D) protein-coding associated mutation class of top 10 mutated CAL protein-coding genes. (E) Mutation frequencies of 22 CAL protein-coding genes in 32 types of cancers.
Fig 2.
Prognostic value of CAL protein-coding associated mutations.
(A) Bubble plots presenting HR of survival differences between patients harboring CAL protein-coding associated mutation and patients wild-type gene pattern in cancers with sample size≥100. (B) Kaplan-Meier plots in BLCA and CESC patients with CAL protein-coding associated gene-set mutation compared to patients with wild type gene pattern. (C) Bubble plots presenting hazard ratios of survival differences between patients with individual CAL proteins-coding associated mutation and wild type in cancers with sample size ≥ 100. (D and E) Forrest-plots presenting HR and 95%CI in UCEC patients with TJP1, USH1C, STIL, PLK4, MED1 or BLNK protein-coding associated mutation compared to patients with wild type gene pattern.
Fig 3.
Correlation between CAL protein-coding associated mutations and improved prognosis in UCEC.
(A) The general landscape of potential somatic IDR associated mutations and (B) non-IDR associated mutations were mapped in UCEC patients. Kaplan-Meier plots for OS and PFS in UCEC patients with (C) IDR enriched mutations, single IDR mutation and wild type, and in patients with (D) enriched CAL protein-coding mutations, single mutation and wild type.
Fig 4.
Prognostic value of CAL protein-coding gene-set at transcriptional level.
(A) GSVA score of CAL protein-coding genes across 31 types of cancers. (B) Bubble plots representing the HRs of survival differences between high vs low CAL protein-coding gene-set GSVA in cancer with cohorts containing at least 100 patients. (C) Kaplan-Meier plots in KIRP, LIHC and LUAD patients with high vs low CAL protein-coding gene GSVA score. (D) Box plots demonstrating the distribution of CAL protein-coding gene-set GSVA among different clinical and pathologic stages in KIRP (E) Bubble plots representing the HRs of survival differences in KIRP, LIHC, LUAD, LGG, PAAD and SARC between patients with high and low CBX2/RNF2, G3BP1/FUS, DACT1/CSNK2A1, PLK4/SASS6, MED1/POU5F1 and YAP1/TEAD1 GSVA scores.
Fig 5.
Correlation between CAL protein-coding associated mutations and immune infiltration pattern.
(A) Volcano plots summarizing the difference of immune infiltration between patients harboring CAL protein-coding associated gene-set mutations and patients with wild type CAL protein-coding genes. (B) Bubble plots representing the immune infiltrates between mutant and wild type of individual CAL protein-coding genes in UCEC. (C) Heatmap representing the spearman correlation between GSVA score of CAL protein-coding gene set and immune infiltrates.
Fig 6.
CAL protein-coding gene associated pathway alteration in pan-cancer.
(A) Enriched KEGG pathway and (B) GO biological processes in patients harboring CAL protein-coding associated gene-set mutations in UCEC, COAD and BRCA. CAL protein-coding genes classified in to pathway score cluster A (C), B (D) and C (E) by the percentages of the correlation between single CAL protein-coding gene transcriptional level and 10 classical cancer associated pathway activities across 31 types of cancer. (F-H) Bubble plots representing the HRs of survival differences between high vs low pathway score cluster A, B and C in cancer cohorts containing at least 100 patients.
Fig 7.
Correlation between GDSC and CTRP drug sensitivity and CAL protein-coding gene transcriptional level.
(A) and (B) Bubble plots represent the top 30 correlated drugs to CAL protein-coding gene expression (left) and sanky plots represent the targets of the drugs (right).
Fig 8.
Pervasive phase separation inhibitor alters cellular sensitivity to chemotherapeutic drugs and cell cycle.
(A) Cellular relative viability of H1299, CAOV3, HeLa and 231 measured by MTT exposed to cDDP alone vs. cDDP plus 2% hexanediol. (B) Cellular relative viability of OVRCA4, SKOV3, CASKI and ES2 measured by MTT exposed to paclitaxel alone vs. paclitaxel plus 2% hexanediol. (C) PCA analysis based on cellular transcriptional level of 22 CAL protein-coding genes and cellular drug responsiveness. (D) Heat map demonstrating relative transcriptional level of 22 CAL protein-coding genes across cells with different cellular drug responsiveness. (E) Representative FACS images of cell cycle distribution of T24 treated with cDDP (10 μM), paclitaxel (10 μM) for 24 hours or treated with cDDP (10 μM), paclitaxel (10 μM) for 12 hours followed by incubation with 2% hexanediol for another 12 hours. (F) The quantification of cell cycle distribution of T24. Mean ± SD of three independent experiments.