Q-omics provides the consensus-scored DPF2 profile across patient tissues and cancer cell-line models. DPF2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, DPF2 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, DPF2 protein abundance shows 25,392 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KICH, HNSC, and LSCC as cancer lineages where DPF2 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 DPF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DPF2 survival associations across molecular data types. DPF2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DPF2 RNA expression–survival associations across cancer types. High DPF2 expression shows unfavorable associations in KICH, LIHC, ACC and COAD, but favorable associations in KIRC and UCS. 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 DPF2 RNA expression.
This table summarizes DPF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 7. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for DPF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DPF2 shows higher tumor expression in HNSC, KIRC, LIHC, COAD, LUSC and BRCA. The HNSC box plot shows higher DPF2 RNA expression in tumor versus normal tissue (log2 FC = +0.894, t-test p < 0.001).
This table shows molecular features associated with DPF2 in patient tissues and cancer cell lines. In patient samples, DPF2 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, DPF2 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 KIDNEY and BLOOD_Leukemia.
