Q-omics provides the consensus-scored GPN2 profile across patient tissues and cancer cell-line models. GPN2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, GPN2 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, GPN2 RNA expression shows 19,580 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KICH, HNSC, and ACC as cancer lineages where GPN2 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 GPN2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GPN2 survival associations across molecular data types. GPN2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (1) 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 GPN2 RNA expression–survival associations across cancer types. High GPN2 expression shows unfavorable associations in KICH, LIHC, ACC and LGG, but favorable associations in SCLC and KIRC. The KICH Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify KICH as the clearest survival context for GPN2 RNA expression.
This table summarizes GPN2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for GPN2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GPN2 shows lower tumor expression in KICH and BRCA and higher tumor expression in HNSC, LIHC, BLCA and STAD. The HNSC box plot shows higher GPN2 RNA expression in tumor versus normal tissue (log2 FC = +0.602, t-test p < 0.001).
This table shows molecular features associated with GPN2 in patient tissues and cancer cell lines. In patient samples, GPN2 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, GPN2 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 BONE and LARGE_INTESTINE.
