Q-omics provides the consensus-scored IGF2-AS profile across patient tissues and cancer cell-line models. IGF2-AS expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, IGF2-AS is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, IGF2-AS RNA expression shows 13,045 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UCS, HNSC, and TGCT as cancer lineages where IGF2-AS 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-AS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IGF2-AS survival associations across molecular data types. IGF2-AS RNA expression shows survival associations in the most cancer types (24), followed by mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IGF2-AS RNA expression–survival associations across cancer types. High IGF2-AS expression shows unfavorable associations in MESO, OV, STAD, LUSC and THCA, but favorable associations in UCS. The UCS Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .004). Together, the overview and detailed table identify UCS as the clearest survival context for IGF2-AS RNA expression.
This table summarizes IGF2-AS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for IGF2-AS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IGF2-AS shows lower tumor expression in UCEC, KICH, KIRP and KIRC and higher tumor expression in HNSC and BLCA. The HNSC box plot shows higher IGF2-AS RNA expression in tumor versus normal tissue (log2 FC = +0.241, t-test p < 0.001).
This table shows molecular features associated with IGF2-AS in patient tissues and cancer cell lines. In patient samples, IGF2-AS 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-AS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in NCI60_ALL.
