activating signal cointegrator 1 complex subunit 2Genealiases: ASC1p100 · p100
Q-omics provides the consensus-scored ASCC2 profile across patient tissues and cancer cell-line models. ASCC2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, ASCC2 is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, ASCC2 protein abundance shows 23,650 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KICH, KIRC, and GBM as cancer lineages where ASCC2 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 ASCC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ASCC2 survival associations across molecular data types. ASCC2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (6) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ASCC2 RNA expression–survival associations across cancer types. High ASCC2 expression shows unfavorable associations in KICH, ACC, HNSC, SKCM and LIHC, but favorable associations in SCLC. The KICH Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify KICH as the clearest survival context for ASCC2 RNA expression.
This table summarizes ASCC2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 10. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ASCC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ASCC2 shows higher tumor expression in KIRC, LIHC, LUSC, HNSC, KIRP and LUAD. The KIRC box plot shows higher ASCC2 RNA expression in tumor versus normal tissue (log2 FC = +0.619, t-test p < 0.001).
This table shows molecular features associated with ASCC2 in patient tissues and cancer cell lines. In patient samples, ASCC2 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, ASCC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
