Q-omics provides the consensus-scored LIPC profile across patient tissues and cancer cell-line models. LIPC expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, LIPC is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, LIPC RNA expression shows 15,682 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight HNSC, KICH, and UVM as cancer lineages where LIPC 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 LIPC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes LIPC survival associations across molecular data types. LIPC RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible LIPC RNA expression–survival associations across cancer types. High LIPC expression shows unfavorable associations in OV, LGG and LAML, but favorable associations in HNSC, KIRC and SKCM. The HNSC 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 HNSC as the clearest survival context for LIPC RNA expression.
This table summarizes LIPC tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 1. The strongest signals are observed in KICH for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for LIPC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. LIPC shows lower tumor expression in KICH, COAD, THCA, UCEC and BRCA and higher tumor expression in STAD. The KICH box plot shows higher LIPC RNA expression in normal versus tumor tissue (log2 FC = −3.210, t-test p < 0.001).
This table shows molecular features associated with LIPC in patient tissues and cancer cell lines. In patient samples, LIPC shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, LIPC 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 BONE and BLOOD_Leukemia.
