Q-omics provides the consensus-scored INS-IGF2 profile across patient tissues and cancer cell-line models. INS-IGF2 expression is associated with patient survival in 12 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, INS-IGF2 is differentially expressed in 7, with the highest sampling consensus in LIHC. Additionally, INS-IGF2 RNA expression shows 7,321 significant gene co-expression associations, with the highest sampling consensus in PCPG. Together, these results highlight KIRC, LIHC, and PCPG as cancer lineages where INS-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 INS-IGF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes INS-IGF2 survival associations across molecular data types. INS-IGF2 RNA expression shows survival associations in the most cancer types (12), followed by mutation status (1) 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 INS-IGF2 RNA expression–survival associations across cancer types. High INS-IGF2 expression shows unfavorable associations in KIRC, MESO, OV and LUSC, but favorable associations in LIHC and UCS. The KIRC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .005). Together, the overview and detailed table identify KIRC as the clearest survival context for INS-IGF2 RNA expression.
This table summarizes INS-IGF2 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 1. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for INS-IGF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. INS-IGF2 shows lower tumor expression in LIHC, THCA, CHOL, KIRC, HNSC and KIRP. The LIHC box plot shows higher INS-IGF2 RNA expression in normal versus tumor tissue (log2 FC = −2.517, t-test p < 0.001).
This table shows molecular features associated with INS-IGF2 in patient tissues and cancer cell lines. In patient samples, INS-IGF2 shows the broadest associations at the RNA and protein expression levels, with PCPG recurring as the lineage with the largest associated feature set. In cancer cell lines, INS-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 OVARY.
