Q-omics provides the consensus-scored C14orf93 profile across patient tissues and cancer cell-line models. C14orf93 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in BRCA. Among the 18 cancer types available for tumor–normal comparison, C14orf93 is differentially expressed in 14, with the highest sampling consensus in LIHC. Additionally, C14orf93 RNA expression shows 20,098 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight BRCA, LIHC, and GBM as cancer lineages where C14orf93 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 C14orf93 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes C14orf93 survival associations across molecular data types. C14orf93 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (4) 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 C14orf93 RNA expression–survival associations across cancer types. High C14orf93 expression shows unfavorable associations in ACC and LIHC, but favorable associations in BRCA, UVM, UCS and MESO. The BRCA 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 BRCA as the clearest survival context for C14orf93 RNA expression.
This table summarizes C14orf93 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 2. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for C14orf93. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. C14orf93 shows lower tumor expression in THCA and KICH and higher tumor expression in LIHC, HNSC, CHOL and ESCA. The LIHC box plot shows higher C14orf93 RNA expression in tumor versus normal tissue (log2 FC = +1.295, t-test p < 0.001).
This table shows molecular features associated with C14orf93 in patient tissues and cancer cell lines. In patient samples, C14orf93 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, C14orf93 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in LIVER and UPPER_AERODIGESTIVE_TRACT.
