Q-omics provides the consensus-scored F9 profile across patient tissues and cancer cell-line models. F9 expression is associated with patient survival in 11 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, F9 is differentially expressed in 3, with the highest sampling consensus in LIHC. Additionally, F9 protein abundance shows 32,767 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, and GBM as cancer lineages where F9 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 F9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes F9 survival associations across molecular data types. F9 RNA expression shows survival associations in the most cancer types (11), followed by mutation status (2) and mass-spec protein abundance (13). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible F9 RNA expression–survival associations across cancer types. High F9 expression shows unfavorable associations in LUAD and CHOL, but favorable associations in LIHC, ESCA, ACC and BLCA. The LIHC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify LIHC as the clearest survival context for F9 RNA expression.
This table summarizes F9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 3, while mass-spec protein shows differences in 13. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for F9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. F9 shows lower tumor expression in LIHC, CHOL and KIRC. The LIHC box plot shows higher F9 RNA expression in normal versus tumor tissue (log2 FC = −3.090, t-test p < 0.001).
This table shows molecular features associated with F9 in patient tissues and cancer cell lines. In patient samples, F9 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, F9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in OESOPHAGUS and UPPER_AERODIGESTIVE_TRACT.
