Q-omics provides the consensus-scored SNX6 profile across patient tissues and cancer cell-line models. SNX6 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, SNX6 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, SNX6 protein abundance shows 29,294 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight LIHC, HNSC, and LSCC as cancer lineages where SNX6 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 SNX6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SNX6 survival associations across molecular data types. SNX6 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SNX6 RNA expression–survival associations across cancer types. High SNX6 expression shows unfavorable associations in LIHC, ACC, KICH, HNSC and SCLC, but favorable associations in UCS. The LIHC 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 LIHC as the clearest survival context for SNX6 RNA expression.
This table summarizes SNX6 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 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SNX6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SNX6 shows lower tumor expression in KICH and higher tumor expression in HNSC, BLCA, LIHC, CHOL and LUAD. The HNSC box plot shows higher SNX6 RNA expression in tumor versus normal tissue (log2 FC = +1.013, t-test p < 0.001).
This table shows molecular features associated with SNX6 in patient tissues and cancer cell lines. In patient samples, SNX6 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, SNX6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in CNS and BLOOD_Leukemia.
