Q-omics provides the consensus-scored SETX profile across patient tissues and cancer cell-line models. SETX expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SETX is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, SETX protein abundance shows 28,899 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where SETX 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 SETX — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SETX survival associations across molecular data types. SETX RNA expression shows survival associations in the most cancer types (22), followed by mutation status (12) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SETX RNA expression–survival associations across cancer types. High SETX expression shows unfavorable associations in ACC, LGG and OV, but favorable associations in KIRC, SCLC and CHOL. 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 SETX RNA expression.
This table summarizes SETX tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 8. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SETX. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SETX shows lower tumor expression in THCA and KICH and higher tumor expression in HNSC, LIHC, CHOL and STAD. The HNSC box plot shows higher SETX RNA expression in tumor versus normal tissue (log2 FC = +0.759, t-test p < 0.001).
This table shows molecular features associated with SETX in patient tissues and cancer cell lines. In patient samples, SETX 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, SETX 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 BLOOD_Leukemia.
