Q-omics provides the consensus-scored LIPF profile across patient tissues and cancer cell-line models. LIPF expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in UCEC. Among the 18 cancer types available for tumor–normal comparison, LIPF is differentially expressed in 7, with the highest sampling consensus in THCA. Additionally, LIPF RNA expression shows 7,016 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UCEC, THCA, and TGCT as cancer lineages where LIPF 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 LIPF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes LIPF survival associations across molecular data types. LIPF RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) 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 LIPF RNA expression–survival associations across cancer types. High LIPF expression shows unfavorable associations in UCEC, LIHC, READ, SKCM and GBM, but favorable associations in MESO. The UCEC 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 UCEC as the clearest survival context for LIPF RNA expression.
This table summarizes LIPF tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7. The strongest signals are observed in THCA for RNA.
This table ranks reproducible tumor–normal expression differences for LIPF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. LIPF shows lower tumor expression in THCA, UCEC, STAD, BRCA, KIRP and ESCA. The THCA box plot shows higher LIPF RNA expression in normal versus tumor tissue (log2 FC = −0.316, t-test p < 0.001).
This table shows molecular features associated with LIPF in patient tissues and cancer cell lines. In patient samples, LIPF 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, LIPF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and LARGE_INTESTINE.
