Q-omics provides the consensus-scored PHB profile across patient tissues and cancer cell-line models. PHB expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, PHB is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, PHB protein abundance shows 18,808 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, COAD, and LSCC as cancer lineages where PHB 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 PHB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PHB survival associations across molecular data types. PHB RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PHB RNA expression–survival associations across cancer types. High PHB expression shows unfavorable associations in HNSC, ACC, UVM, LIHC, BLCA and LUAD. The HNSC 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 HNSC as the clearest survival context for PHB RNA expression.
This table summarizes PHB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 7. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PHB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PHB shows lower tumor expression in KIRC and higher tumor expression in COAD, LIHC, STAD, LUAD and LUSC. The COAD box plot shows higher PHB RNA expression in tumor versus normal tissue (log2 FC = +1.020, t-test p < 0.001).
This table shows molecular features associated with PHB in patient tissues and cancer cell lines. In patient samples, PHB shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, PHB 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 UPPER_AERODIGESTIVE_TRACT and SKIN.
