Q-omics provides the consensus-scored SRF profile across patient tissues and cancer cell-line models. SRF expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, SRF is differentially expressed in 13, with the highest sampling consensus in BLCA. Additionally, SRF protein abundance shows 21,548 significant protein co-abundance associations, with the highest sampling consensus in UCEC. Together, these results highlight BLCA, and UCEC as cancer lineages where SRF 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 SRF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SRF survival associations across molecular data types. SRF RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) 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 SRF RNA expression–survival associations across cancer types. High SRF expression shows unfavorable associations in BLCA, COAD, ACC and LUAD, but favorable associations in KIRC and SCLC. The BLCA 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 BLCA as the clearest survival context for SRF RNA expression.
This table summarizes SRF 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 4. The strongest signals are observed in BLCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SRF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SRF shows lower tumor expression in BLCA, THCA, KICH, UCEC, LUAD and BRCA. The BLCA box plot shows higher SRF RNA expression in normal versus tumor tissue (log2 FC = −2.338, t-test p < 0.001).
This table shows molecular features associated with SRF in patient tissues and cancer cell lines. In patient samples, SRF shows the broadest associations at the RNA and protein expression levels, with UCEC recurring as the lineage with the largest associated feature set. In cancer cell lines, SRF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Leukemia.
