Q-omics provides the consensus-scored IGF2R profile across patient tissues and cancer cell-line models. IGF2R expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, IGF2R is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, IGF2R RNA expression shows 19,435 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight MESO, HNSC, and ACC as cancer lineages where IGF2R 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 IGF2R — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IGF2R survival associations across molecular data types. IGF2R RNA expression shows survival associations in the most cancer types (28), followed by mutation status (10) 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 IGF2R RNA expression–survival associations across cancer types. High IGF2R expression shows unfavorable associations in MESO, BLCA, CESC, LIHC and OV, but favorable associations in KIRC. The MESO 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 MESO as the clearest survival context for IGF2R RNA expression.
This table summarizes IGF2R 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 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for IGF2R. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IGF2R shows higher tumor expression in HNSC, LIHC, THCA, STAD, KIRP and BLCA. The HNSC box plot shows higher IGF2R RNA expression in tumor versus normal tissue (log2 FC = +1.615, t-test p < 0.001).
This table shows molecular features associated with IGF2R in patient tissues and cancer cell lines. In patient samples, IGF2R 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, IGF2R 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 BREAST and BLOOD_Lymphoma.
