Q-omics provides the consensus-scored FGFR1OP2 profile across patient tissues and cancer cell-line models. FGFR1OP2 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, FGFR1OP2 is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, FGFR1OP2 RNA expression shows 20,205 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight SKCM, HNSC, and UVM as cancer lineages where FGFR1OP2 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 FGFR1OP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGFR1OP2 survival associations across molecular data types. FGFR1OP2 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (1) 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 FGFR1OP2 RNA expression–survival associations across cancer types. High FGFR1OP2 expression shows unfavorable associations in UVM, CESC and KICH, but favorable associations in SKCM, KIRC and CHOL. The SKCM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify SKCM as the clearest survival context for FGFR1OP2 RNA expression.
This table summarizes FGFR1OP2 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 HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for FGFR1OP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGFR1OP2 shows lower tumor expression in UCEC, BRCA, KICH and LUAD and higher tumor expression in HNSC and CHOL. The HNSC box plot shows higher FGFR1OP2 RNA expression in tumor versus normal tissue (log2 FC = +0.589, t-test p < 0.001).
This table shows molecular features associated with FGFR1OP2 in patient tissues and cancer cell lines. In patient samples, FGFR1OP2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, FGFR1OP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
