Q-omics provides the consensus-scored C9 profile across patient tissues and cancer cell-line models. C9 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, C9 is differentially expressed in 12, with the highest sampling consensus in LIHC. Additionally, C9 protein abundance shows 23,743 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, LIHC, and PDAC as cancer lineages where C9 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 C9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes C9 survival associations across molecular data types. C9 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (8) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible C9 RNA expression–survival associations across cancer types. High C9 expression shows unfavorable associations in ACC, STAD, LUAD and KIRP, but favorable associations in ESCA and LUSC. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify ACC as the clearest survival context for C9 RNA expression.
This table summarizes C9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for C9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. C9 shows lower tumor expression in LIHC, KIRP, KIRC and CHOL and higher tumor expression in HNSC and LUSC. The LIHC box plot shows higher C9 RNA expression in normal versus tumor tissue (log2 FC = −6.154, t-test p < 0.001).
This table shows molecular features associated with C9 in patient tissues and cancer cell lines. In patient samples, C9 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, C9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in STOMACH and BLOOD_Leukemia.
