Q-omics provides the consensus-scored SSH3 profile across patient tissues and cancer cell-line models. SSH3 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in LGG. Among the 18 cancer types available for tumor–normal comparison, SSH3 is differentially expressed in 14, with the highest sampling consensus in KIRP. Additionally, SSH3 protein abundance shows 23,078 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight LGG, KIRP, and BRCA as cancer lineages where SSH3 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 SSH3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SSH3 survival associations across molecular data types. SSH3 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SSH3 RNA expression–survival associations across cancer types. High SSH3 expression shows unfavorable associations in LGG and PAAD, but favorable associations in BLCA, BRCA, UCEC and SARC. The LGG 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 LGG as the clearest survival context for SSH3 RNA expression.
This table summarizes SSH3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRP for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SSH3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SSH3 shows higher tumor expression in KIRP, LUAD, THCA, LIHC, BRCA and CHOL. The KIRP box plot shows higher SSH3 RNA expression in tumor versus normal tissue (log2 FC = +1.160, t-test p < 0.001).
This table shows molecular features associated with SSH3 in patient tissues and cancer cell lines. In patient samples, SSH3 shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, SSH3 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 URINARY_TRACT and BONE.
