Q-omics provides the consensus-scored SFPQ profile across patient tissues and cancer cell-line models. SFPQ expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SFPQ is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, SFPQ protein abundance shows 38,899 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where SFPQ 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 SFPQ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SFPQ survival associations across molecular data types. SFPQ RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SFPQ RNA expression–survival associations across cancer types. High SFPQ expression shows unfavorable associations in ACC, LIHC and SARC, but favorable associations in SCLC, BRCA and UCS. The ACC 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 ACC as the clearest survival context for SFPQ RNA expression.
This table summarizes SFPQ tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 11. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SFPQ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SFPQ shows lower tumor expression in KICH and higher tumor expression in HNSC, COAD, LIHC, STAD and LUSC. The HNSC box plot shows higher SFPQ RNA expression in tumor versus normal tissue (log2 FC = +0.782, t-test p < 0.001).
This table shows molecular features associated with SFPQ in patient tissues and cancer cell lines. In patient samples, SFPQ 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, SFPQ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
