selectin P ligandGenealiases: CD162 · CLA · PSGL-1 · PSGL1
Q-omics provides the consensus-scored SELPLG profile across patient tissues and cancer cell-line models. SELPLG expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, SELPLG is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, SELPLG RNA expression shows 23,185 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, KIRC, and LSCC as cancer lineages where SELPLG 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 SELPLG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SELPLG survival associations across molecular data types. SELPLG RNA expression shows survival associations in the most cancer types (21), followed by mutation status (5) 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 SELPLG RNA expression–survival associations across cancer types. High SELPLG expression shows unfavorable associations in LAML and UVM, but favorable associations in HNSC, SKCM, CESC and UCEC. 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 SELPLG RNA expression.
This table summarizes SELPLG 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 KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SELPLG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SELPLG shows lower tumor expression in LUAD and LUSC and higher tumor expression in KIRC, KIRP, THCA and HNSC. The KIRC box plot shows higher SELPLG RNA expression in tumor versus normal tissue (log2 FC = +2.579, t-test p < 0.001).
This table shows molecular features associated with SELPLG in patient tissues and cancer cell lines. In patient samples, SELPLG 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, SELPLG RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BONE.
