Q-omics provides the consensus-scored INHBA profile across patient tissues and cancer cell-line models. INHBA expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, INHBA is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, INHBA protein abundance shows 20,290 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight KIRP, HNSC, and LUAD as cancer lineages where INHBA 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 INHBA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes INHBA survival associations across molecular data types. INHBA RNA expression shows survival associations in the most cancer types (27), followed by mutation status (10) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible INHBA RNA expression–survival associations across cancer types. High INHBA expression shows unfavorable associations in KIRP, MESO, HNSC, BLCA, LUSC and CESC. The KIRP 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 KIRP as the clearest survival context for INHBA RNA expression.
This table summarizes INHBA 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 CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for INHBA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. INHBA shows higher tumor expression in HNSC, COAD, BLCA, STAD, BRCA and READ. The HNSC box plot shows higher INHBA RNA expression in tumor versus normal tissue (log2 FC = +4.591, t-test p < 0.001).
This table shows molecular features associated with INHBA in patient tissues and cancer cell lines. In patient samples, INHBA shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, INHBA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BONE.
