Q-omics provides the consensus-scored AHSG profile across patient tissues and cancer cell-line models. AHSG 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, AHSG is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, AHSG protein abundance shows 27,886 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KIRP, KICH, and PDAC as cancer lineages where AHSG 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 AHSG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AHSG survival associations across molecular data types. AHSG RNA expression shows survival associations in the most cancer types (27), followed by mutation status (6) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AHSG RNA expression–survival associations across cancer types. High AHSG expression shows unfavorable associations in KIRP, LUAD and CHOL, but favorable associations in READ, LGG and DLBC. 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 AHSG RNA expression.
This table summarizes AHSG tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in KICH for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for AHSG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AHSG shows lower tumor expression in KICH, KIRC and CHOL and higher tumor expression in LUSC, LUAD and THCA. The KICH box plot shows higher AHSG RNA expression in normal versus tumor tissue (log2 FC = −0.509, t-test p < 0.001).
This table shows molecular features associated with AHSG in patient tissues and cancer cell lines. In patient samples, AHSG shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, AHSG RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BONE.
