Q-omics provides the consensus-scored API5 profile across patient tissues and cancer cell-line models. API5 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, API5 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, API5 protein abundance shows 25,579 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, HNSC, and GBM as cancer lineages where API5 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 API5 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes API5 survival associations across molecular data types. API5 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible API5 RNA expression–survival associations across cancer types. High API5 expression shows unfavorable associations in ACC and LIHC, but favorable associations in KIRC, READ, UCS and OV. The KIRC 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 KIRC as the clearest survival context for API5 RNA expression.
This table summarizes API5 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 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for API5. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. API5 shows lower tumor expression in THCA and KIRC and higher tumor expression in HNSC, LIHC, STAD and CHOL. The HNSC box plot shows higher API5 RNA expression in tumor versus normal tissue (log2 FC = +0.657, t-test p < 0.001).
This table shows molecular features associated with API5 in patient tissues and cancer cell lines. In patient samples, API5 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, API5 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_Myeloma and BLOOD_Leukemia.
