Q-omics provides the consensus-scored IGFN1 profile across patient tissues and cancer cell-line models. IGFN1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, IGFN1 is differentially expressed in 12, with the highest sampling consensus in KIRP. Additionally, IGFN1 RNA expression shows 12,803 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRC, KIRP, and TGCT as cancer lineages where IGFN1 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 IGFN1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IGFN1 survival associations across molecular data types. IGFN1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) 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 IGFN1 RNA expression–survival associations across cancer types. High IGFN1 expression shows unfavorable associations in KIRC, ACC, UVM, KICH and LUSC, but favorable associations in LGG. 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 IGFN1 RNA expression.
This table summarizes IGFN1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 1. The strongest signals are observed in KIRP for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for IGFN1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IGFN1 shows lower tumor expression in UCEC, THCA and LUSC and higher tumor expression in KIRP, COAD and KIRC. The KIRP box plot shows higher IGFN1 RNA expression in tumor versus normal tissue (log2 FC = +2.321, t-test p < 0.001).
This table shows molecular features associated with IGFN1 in patient tissues and cancer cell lines. In patient samples, IGFN1 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, IGFN1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and LIVER.
