Q-omics provides the consensus-scored GPNMB profile across patient tissues and cancer cell-line models. GPNMB expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, GPNMB is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, GPNMB protein abundance shows 22,699 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, HNSC, and LSCC as cancer lineages where GPNMB 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 GPNMB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GPNMB survival associations across molecular data types. GPNMB RNA expression shows survival associations in the most cancer types (24), followed by mutation status (6) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GPNMB RNA expression–survival associations across cancer types. High GPNMB expression shows unfavorable associations in UVM, MESO, STAD, LGG and ACC, but favorable associations in DLBC. The UVM 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 UVM as the clearest survival context for GPNMB RNA expression.
This table summarizes GPNMB 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 10. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for GPNMB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GPNMB shows lower tumor expression in UCEC and BRCA and higher tumor expression in HNSC, KIRP, KIRC and KICH. The HNSC box plot shows higher GPNMB RNA expression in tumor versus normal tissue (log2 FC = +2.287, t-test p < 0.001).
This table shows molecular features associated with GPNMB in patient tissues and cancer cell lines. In patient samples, GPNMB 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, GPNMB 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.
