Q-omics provides the consensus-scored IGF1 profile across patient tissues and cancer cell-line models. IGF1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, IGF1 is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, IGF1 RNA expression shows 18,739 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight UVM, COAD, and BRCA as cancer lineages where IGF1 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 IGF1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IGF1 survival associations across molecular data types. IGF1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IGF1 RNA expression–survival associations across cancer types. High IGF1 expression shows unfavorable associations in UVM, BLCA, LUSC and ESCA, but favorable associations in LIHC and UCEC. 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 IGF1 RNA expression.
This table summarizes IGF1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 5. The strongest signals are observed in COAD for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for IGF1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IGF1 shows lower tumor expression in COAD, LIHC, THCA, BRCA, BLCA and KIRP. The COAD box plot shows higher IGF1 RNA expression in normal versus tumor tissue (log2 FC = −0.792, t-test p < 0.001).
This table shows molecular features associated with IGF1 in patient tissues and cancer cell lines. In patient samples, IGF1 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, IGF1 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 BONE and SKIN.
