Q-omics provides the consensus-scored ACTG1 profile across patient tissues and cancer cell-line models. ACTG1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ACTG1 is differentially expressed in 14, with the highest sampling consensus in LIHC. Additionally, ACTG1 protein abundance shows 25,154 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight MESO, LIHC, and PDAC as cancer lineages where ACTG1 shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for ACTG1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACTG1 survival associations across molecular data types. ACTG1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ACTG1 RNA expression–survival associations across cancer types. High ACTG1 expression shows unfavorable associations in MESO, KIRP, ACC, LGG and PAAD, but favorable associations in COAD. The MESO Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify MESO as the clearest survival context for ACTG1 RNA expression.
This table summarizes ACTG1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ACTG1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACTG1 shows lower tumor expression in KICH and higher tumor expression in LIHC, LUAD, KIRP, BRCA and HNSC. The LIHC box plot shows higher ACTG1 RNA expression in tumor versus normal tissue (log2 FC = +0.877, t-test p < 0.001).
This table shows molecular features associated with ACTG1 in patient tissues and cancer cell lines. In patient samples, ACTG1 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, ACTG1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
