Q-omics provides the consensus-scored IQCF5 profile across patient tissues and cancer cell-line models. IQCF5 expression is associated with patient survival in 12 of 34 cancer types, with the highest sampling consensus in BLCA. Additionally, IQCF5 RNA expression shows 8,783 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight BLCA, and TGCT as cancer lineages where IQCF5 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 IQCF5 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IQCF5 survival associations across molecular data types. IQCF5 RNA expression shows survival associations in the most cancer types (12). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IQCF5 RNA expression–survival associations across cancer types. High IQCF5 expression shows unfavorable associations in BLCA, ACC, CESC, OV, STAD and LUAD. The BLCA Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .003). Together, the overview and detailed table identify BLCA as the clearest survival context for IQCF5 RNA expression.
This table shows molecular features associated with IQCF5 in patient tissues and cancer cell lines. In patient samples, IQCF5 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, IQCF5 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in CNS and BREAST.
