Q-omics provides the consensus-scored OLFML2B profile across patient tissues and cancer cell-line models. OLFML2B expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, OLFML2B is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, OLFML2B RNA expression shows 23,904 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where OLFML2B 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 OLFML2B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OLFML2B survival associations across molecular data types. OLFML2B RNA expression shows survival associations in the most cancer types (27), followed by mutation status (12) 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 OLFML2B RNA expression–survival associations across cancer types. High OLFML2B expression shows unfavorable associations in ACC, KIRP, UVM, STAD, MESO and LIHC. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify ACC as the clearest survival context for OLFML2B RNA expression.
This table summarizes OLFML2B tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for OLFML2B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. OLFML2B shows higher tumor expression in HNSC, KIRC, COAD, LIHC, STAD and KIRP. The HNSC box plot shows higher OLFML2B RNA expression in tumor versus normal tissue (log2 FC = +2.860, t-test p < 0.001).
This table shows molecular features associated with OLFML2B in patient tissues and cancer cell lines. In patient samples, OLFML2B 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, OLFML2B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BONE.
