Q-omics provides the consensus-scored ABCF3 profile across patient tissues and cancer cell-line models. ABCF3 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, ABCF3 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, ABCF3 RNA expression shows 19,810 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight LIHC, HNSC, and THYM as cancer lineages where ABCF3 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 ABCF3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ABCF3 survival associations across molecular data types. ABCF3 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ABCF3 RNA expression–survival associations across cancer types. High ABCF3 expression shows unfavorable associations in LIHC, KICH, LUAD and KIRC, but favorable associations in UVM and SCLC. The LIHC 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 LIHC as the clearest survival context for ABCF3 RNA expression.
This table summarizes ABCF3 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 HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ABCF3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ABCF3 shows lower tumor expression in THCA and higher tumor expression in HNSC, BLCA, LIHC, COAD and LUSC. The HNSC box plot shows higher ABCF3 RNA expression in tumor versus normal tissue (log2 FC = +1.209, t-test p < 0.001).
This table shows molecular features associated with ABCF3 in patient tissues and cancer cell lines. In patient samples, ABCF3 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, ABCF3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in CNS and BLOOD_Lymphoma.
