Q-omics provides the consensus-scored C17orf49 profile across patient tissues and cancer cell-line models. C17orf49 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, C17orf49 is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, C17orf49 RNA expression shows 15,891 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight KIRC, and KIRP as cancer lineages where C17orf49 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 C17orf49 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes C17orf49 survival associations across molecular data types. C17orf49 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (2) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible C17orf49 RNA expression–survival associations across cancer types. High C17orf49 expression shows unfavorable associations in KIRC, ACC and PRAD, but favorable associations in CESC, DLBC and ESCA. The KIRC 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 KIRC as the clearest survival context for C17orf49 RNA expression.
This table summarizes C17orf49 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 2. The strongest signals are observed in KIRC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for C17orf49. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. C17orf49 shows lower tumor expression in KICH and UCEC and higher tumor expression in KIRC, LIHC, HNSC and BLCA. The KIRC box plot shows higher C17orf49 RNA expression in tumor versus normal tissue (log2 FC = +0.533, t-test p < 0.001).
This table shows molecular features associated with C17orf49 in patient tissues and cancer cell lines. In patient samples, C17orf49 shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, C17orf49 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and UPPER_AERODIGESTIVE_TRACT.
