Q-omics provides the consensus-scored PFKL profile across patient tissues and cancer cell-line models. PFKL expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, PFKL is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, PFKL RNA expression shows 17,758 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight SCLC, KICH, and ACC as cancer lineages where PFKL 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 PFKL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PFKL survival associations across molecular data types. PFKL RNA expression shows survival associations in the most cancer types (25), followed by mutation status (5) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PFKL RNA expression–survival associations across cancer types. High PFKL expression shows unfavorable associations in BRCA, LGG, MESO and LAML, but favorable associations in SCLC and DLBC. The SCLC 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 SCLC as the clearest survival context for PFKL RNA expression.
This table summarizes PFKL 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 4. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PFKL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PFKL shows lower tumor expression in KICH and higher tumor expression in HNSC, KIRC, LIHC, LUSC and UCEC. The KICH box plot shows higher PFKL RNA expression in normal versus tumor tissue (log2 FC = −1.486, t-test p < 0.001).
This table shows molecular features associated with PFKL in patient tissues and cancer cell lines. In patient samples, PFKL shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, PFKL 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_Leukemia and UPPER_AERODIGESTIVE_TRACT.
