Q-omics provides the consensus-scored IAPP profile across patient tissues and cancer cell-line models. IAPP expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, IAPP is differentially expressed in 7, with the highest sampling consensus in LUSC. Additionally, IAPP RNA expression shows 10,803 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight ACC, LUSC, and HNSC as cancer lineages where IAPP 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 IAPP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IAPP survival associations across molecular data types. IAPP RNA expression shows survival associations in the most cancer types (19), followed by mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IAPP RNA expression–survival associations across cancer types. High IAPP expression shows unfavorable associations in ACC, KIRC, UVM, READ, LUAD and COAD. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify ACC as the clearest survival context for IAPP RNA expression.
This table summarizes IAPP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 2. The strongest signals are observed in LUSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for IAPP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IAPP shows lower tumor expression in KICH and KIRC and higher tumor expression in LUSC, HNSC, PRAD and LUAD. The LUSC box plot shows higher IAPP RNA expression in tumor versus normal tissue (log2 FC = +0.107, t-test p < 0.001).
This table shows molecular features associated with IAPP in patient tissues and cancer cell lines. In patient samples, IAPP shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, IAPP 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 CNS and BREAST.
