Q-omics provides the consensus-scored HACL1 profile across patient tissues and cancer cell-line models. HACL1 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, HACL1 is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, HACL1 RNA expression shows 19,864 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KIRC as cancer lineages where HACL1 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 HACL1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HACL1 survival associations across molecular data types. HACL1 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (6) 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 HACL1 RNA expression–survival associations across cancer types. High HACL1 expression shows unfavorable associations in ACC and MESO, but favorable associations in KIRC, READ, THYM and BRCA. The ACC 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 ACC as the clearest survival context for HACL1 RNA expression.
This table summarizes HACL1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, 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 HACL1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HACL1 shows lower tumor expression in KIRC, THCA, LUAD, KICH and KIRP and higher tumor expression in COAD. The KIRC box plot shows higher HACL1 RNA expression in normal versus tumor tissue (log2 FC = −0.810, t-test p < 0.001).
This table shows molecular features associated with HACL1 in patient tissues and cancer cell lines. In patient samples, HACL1 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, HACL1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and SKIN.
