Q-omics provides the consensus-scored ADIPOQ profile across patient tissues and cancer cell-line models. ADIPOQ expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in ESCA. Among the 18 cancer types available for tumor–normal comparison, ADIPOQ is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, ADIPOQ protein abundance shows 27,052 significant protein co-abundance associations, with the highest sampling consensus in UCEC. Together, these results highlight ESCA, HNSC, and UCEC as cancer lineages where ADIPOQ 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 ADIPOQ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ADIPOQ survival associations across molecular data types. ADIPOQ RNA expression shows survival associations in the most cancer types (20), followed by mutation status (2) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ADIPOQ RNA expression–survival associations across cancer types. High ADIPOQ expression shows unfavorable associations in UCS, ACC and CESC, but favorable associations in ESCA, BRCA and PAAD. The ESCA 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 ESCA as the clearest survival context for ADIPOQ RNA expression.
This table summarizes ADIPOQ tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ADIPOQ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ADIPOQ shows lower tumor expression in HNSC, KIRC, BRCA, COAD, STAD and THCA. The HNSC box plot shows higher ADIPOQ RNA expression in normal versus tumor tissue (log2 FC = −2.710, t-test p < 0.001).
This table shows molecular features associated with ADIPOQ in patient tissues and cancer cell lines. In patient samples, ADIPOQ shows the broadest associations at the RNA and protein expression levels, with UCEC recurring as the lineage with the largest associated feature set. In cancer cell lines, ADIPOQ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Myeloma.
