IGF like family member 2Genealiases: UNQ645 · VPRI645
Q-omics provides the consensus-scored IGFL2 profile across patient tissues and cancer cell-line models. IGFL2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, IGFL2 is differentially expressed in 12, with the highest sampling consensus in BLCA. Additionally, IGFL2 RNA expression shows 14,153 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KIRC, BLCA, and PDAC as cancer lineages where IGFL2 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 IGFL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IGFL2 survival associations across molecular data types. IGFL2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IGFL2 RNA expression–survival associations across cancer types. High IGFL2 expression shows unfavorable associations in KIRC, MESO, KIRP, BLCA and ACC, but favorable associations in ESCA. The KIRC 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 KIRC as the clearest survival context for IGFL2 RNA expression.
This table summarizes IGFL2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for IGFL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IGFL2 shows higher tumor expression in BLCA, HNSC, THCA, LUAD, LUSC and COAD. The BLCA box plot shows higher IGFL2 RNA expression in tumor versus normal tissue (log2 FC = +2.294, t-test p < 0.001).
This table shows molecular features associated with IGFL2 in patient tissues and cancer cell lines. In patient samples, IGFL2 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, IGFL2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and SOFT_TISSUE.
