Q-omics provides the consensus-scored SELP profile across patient tissues and cancer cell-line models. SELP expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, SELP is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, SELP protein abundance shows 20,563 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, KIRC, and LSCC as cancer lineages where SELP 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 SELP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SELP survival associations across molecular data types. SELP RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) 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 SELP RNA expression–survival associations across cancer types. High SELP expression shows unfavorable associations in KIRP, but favorable associations in HNSC, CESC, KIRC, LUAD and LIHC. The HNSC 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 HNSC as the clearest survival context for SELP RNA expression.
This table summarizes SELP 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 6. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SELP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SELP shows lower tumor expression in KIRC, KICH, LUAD, KIRP, LUSC and BLCA. The KIRC box plot shows higher SELP RNA expression in normal versus tumor tissue (log2 FC = −1.737, t-test p < 0.001).
This table shows molecular features associated with SELP in patient tissues and cancer cell lines. In patient samples, SELP 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, SELP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in CNS and LARGE_INTESTINE.
