Q-omics provides the consensus-scored SNX30 profile across patient tissues and cancer cell-line models. SNX30 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SNX30 is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, SNX30 protein abundance shows 27,587 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, HNSC, and GBM as cancer lineages where SNX30 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 SNX30 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SNX30 survival associations across molecular data types. SNX30 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) 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 SNX30 RNA expression–survival associations across cancer types. High SNX30 expression shows unfavorable associations in ACC, BLCA and STAD, but favorable associations in KIRC, LUAD and SCLC. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for SNX30 RNA expression.
This table summarizes SNX30 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 4. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SNX30. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SNX30 shows lower tumor expression in LUAD, LUSC and COAD and higher tumor expression in HNSC, THCA and LIHC. The HNSC box plot shows higher SNX30 RNA expression in tumor versus normal tissue (log2 FC = +0.908, t-test p < 0.001).
This table shows molecular features associated with SNX30 in patient tissues and cancer cell lines. In patient samples, SNX30 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, SNX30 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BONE.
