Q-omics provides the consensus-scored SEMG2 profile across patient tissues and cancer cell-line models. SEMG2 expression is associated with patient survival in 15 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, SEMG2 is differentially expressed in 8, with the highest sampling consensus in KIRC. Additionally, SEMG2 protein abundance shows 9,496 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight UCS, KIRC, and PDAC as cancer lineages where SEMG2 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 SEMG2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SEMG2 survival associations across molecular data types. SEMG2 RNA expression shows survival associations in the most cancer types (15), followed by mutation status (7) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SEMG2 RNA expression–survival associations across cancer types. High SEMG2 expression shows unfavorable associations in UCS, KICH, ACC, DLBC, KIRC and ESCA. The UCS 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 UCS as the clearest survival context for SEMG2 RNA expression.
This table summarizes SEMG2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SEMG2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SEMG2 shows lower tumor expression in KIRC, KICH, KIRP and PRAD and higher tumor expression in BRCA and COAD. The KIRC box plot shows higher SEMG2 RNA expression in normal versus tumor tissue (log2 FC = −1.717, t-test p < 0.001).
This table shows molecular features associated with SEMG2 in patient tissues and cancer cell lines. In patient samples, SEMG2 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, SEMG2 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 BLOOD_Leukemia and LARGE_INTESTINE.
