Q-omics provides the consensus-scored SRMS profile across patient tissues and cancer cell-line models. SRMS expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, SRMS is differentially expressed in 16, with the highest sampling consensus in COAD. Additionally, SRMS RNA expression shows 17,382 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight STAD, COAD, and ACC as cancer lineages where SRMS 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 SRMS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SRMS survival associations across molecular data types. SRMS RNA expression shows survival associations in the most cancer types (23), followed by mutation status (10) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SRMS RNA expression–survival associations across cancer types. High SRMS expression shows unfavorable associations in ACC and UVM, but favorable associations in STAD, HNSC, UCEC and BLCA. The STAD Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .002). Together, the overview and detailed table identify STAD as the clearest survival context for SRMS RNA expression.
This table summarizes SRMS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 4. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SRMS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SRMS shows higher tumor expression in COAD, BLCA, LUAD, STAD, BRCA and HNSC. The COAD box plot shows higher SRMS RNA expression in tumor versus normal tissue (log2 FC = +2.372, t-test p < 0.001).
This table shows molecular features associated with SRMS in patient tissues and cancer cell lines. In patient samples, SRMS 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, SRMS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BREAST.
