Q-omics provides the consensus-scored HAGHL profile across patient tissues and cancer cell-line models. HAGHL expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, HAGHL is differentially expressed in 16, with the highest sampling consensus in KICH. Additionally, HAGHL protein abundance shows 17,428 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight UVM, KICH, and BRCA as cancer lineages where HAGHL 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 HAGHL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HAGHL survival associations across molecular data types. HAGHL RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) 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 HAGHL RNA expression–survival associations across cancer types. High HAGHL expression shows unfavorable associations in UVM, UCS, ACC, KIRC and SKCM, but favorable associations in KIRP. The UVM 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 UVM as the clearest survival context for HAGHL RNA expression.
This table summarizes HAGHL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 5. The strongest signals are observed in BLCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for HAGHL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HAGHL shows higher tumor expression in KICH, COAD, BLCA, THCA, HNSC and KIRP. The KICH box plot shows higher HAGHL RNA expression in tumor versus normal tissue (log2 FC = +3.650, t-test p < 0.001).
This table shows molecular features associated with HAGHL in patient tissues and cancer cell lines. In patient samples, HAGHL shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, HAGHL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and SKIN.
