Q-omics provides the consensus-scored C4orf50 profile across patient tissues and cancer cell-line models. C4orf50 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, C4orf50 is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, C4orf50 RNA expression shows 15,824 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight SKCM, KIRC, and GBM as cancer lineages where C4orf50 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 C4orf50 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes C4orf50 survival associations across molecular data types. C4orf50 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible C4orf50 RNA expression–survival associations across cancer types. High C4orf50 expression shows unfavorable associations in ACC and KIRP, but favorable associations in SKCM, HNSC, SARC and MESO. The SKCM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify SKCM as the clearest survival context for C4orf50 RNA expression.
This table summarizes C4orf50 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for C4orf50. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. C4orf50 shows lower tumor expression in THCA and KICH and higher tumor expression in KIRC, HNSC, BRCA and LIHC. The KIRC box plot shows higher C4orf50 RNA expression in tumor versus normal tissue (log2 FC = +0.080, t-test p < 0.001).
This table shows molecular features associated with C4orf50 in patient tissues and cancer cell lines. In patient samples, C4orf50 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, C4orf50 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LARGE_INTESTINE.
