Q-omics provides the consensus-scored IGF2 profile across patient tissues and cancer cell-line models. IGF2 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, IGF2 is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, IGF2 RNA expression shows 14,586 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRP, KICH, and TGCT as cancer lineages where IGF2 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 IGF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IGF2 survival associations across molecular data types. IGF2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IGF2 RNA expression–survival associations across cancer types. High IGF2 expression shows unfavorable associations in KIRP, UVM, MESO, OV and SKCM, but favorable associations in UCS. 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 IGF2 RNA expression.
This table summarizes IGF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for IGF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IGF2 shows lower tumor expression in KICH, KIRC, KIRP, UCEC and THCA and higher tumor expression in HNSC. The KICH box plot shows higher IGF2 RNA expression in normal versus tumor tissue (log2 FC = −4.852, t-test p < 0.001).
This table shows molecular features associated with IGF2 in patient tissues and cancer cell lines. In patient samples, IGF2 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, IGF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and OVARY.
