Q-omics provides the consensus-scored SSR1 profile across patient tissues and cancer cell-line models. SSR1 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SSR1 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, SSR1 protein abundance shows 36,577 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, HNSC, and PDAC as cancer lineages where SSR1 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 SSR1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SSR1 survival associations across molecular data types. SSR1 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (3) and mass-spec protein abundance (13). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SSR1 RNA expression–survival associations across cancer types. High SSR1 expression shows unfavorable associations in ACC, KIRP, LIHC, KICH and SARC, but favorable associations in COAD. 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 SSR1 RNA expression.
This table summarizes SSR1 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 13. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SSR1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SSR1 shows lower tumor expression in THCA and higher tumor expression in HNSC, BLCA, LIHC, LUAD and KIRC. The HNSC box plot shows higher SSR1 RNA expression in tumor versus normal tissue (log2 FC = +0.769, t-test p < 0.001).
This table shows molecular features associated with SSR1 in patient tissues and cancer cell lines. In patient samples, SSR1 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, SSR1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in SKIN and LARGE_INTESTINE.
