Q-omics provides the consensus-scored SV2A profile across patient tissues and cancer cell-line models. SV2A expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, SV2A is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, SV2A RNA expression shows 22,227 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight MESO, KICH, and GBM as cancer lineages where SV2A 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 SV2A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SV2A survival associations across molecular data types. SV2A RNA expression shows survival associations in the most cancer types (26), followed by mutation status (8) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SV2A RNA expression–survival associations across cancer types. High SV2A expression shows unfavorable associations in MESO, BLCA and CESC, but favorable associations in THCA, PAAD and SKCM. The MESO Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify MESO as the clearest survival context for SV2A RNA expression.
This table summarizes SV2A 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 KICH for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for SV2A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SV2A shows lower tumor expression in KICH and COAD and higher tumor expression in LUAD, LUSC, HNSC and THCA. The KICH box plot shows higher SV2A RNA expression in normal versus tumor tissue (log2 FC = −1.449, t-test p < 0.001).
This table shows molecular features associated with SV2A in patient tissues and cancer cell lines. In patient samples, SV2A 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, SV2A 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 LUNG_NSCLC_LUAD and BLOOD_Leukemia.
