Q-omics provides the consensus-scored APLNR profile across patient tissues and cancer cell-line models. APLNR expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, APLNR is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, APLNR RNA expression shows 18,152 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, and GBM as cancer lineages where APLNR 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 APLNR — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APLNR survival associations across molecular data types. APLNR RNA expression shows survival associations in the most cancer types (22), followed by mutation status (6) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APLNR RNA expression–survival associations across cancer types. High APLNR expression shows unfavorable associations in KIRP, MESO, UVM, STAD and BLCA, but favorable associations in KIRC. 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 APLNR RNA expression.
This table summarizes APLNR 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 4. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for APLNR. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APLNR shows lower tumor expression in KIRP and LUAD and higher tumor expression in KIRC, STAD, HNSC and THCA. The KIRC box plot shows higher APLNR RNA expression in tumor versus normal tissue (log2 FC = +2.373, t-test p < 0.001).
This table shows molecular features associated with APLNR in patient tissues and cancer cell lines. In patient samples, APLNR 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, APLNR RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
