Q-omics provides the consensus-scored TERF2IP profile across patient tissues and cancer cell-line models. TERF2IP expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in DLBC. Among the 18 cancer types available for tumor–normal comparison, TERF2IP is differentially expressed in 10, with the highest sampling consensus in THCA. Additionally, TERF2IP RNA expression shows 19,399 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight DLBC, THCA, and UVM as cancer lineages where TERF2IP 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 TERF2IP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TERF2IP survival associations across molecular data types. TERF2IP RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) 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 TERF2IP RNA expression–survival associations across cancer types. High TERF2IP expression shows unfavorable associations in DLBC and HNSC, but favorable associations in PAAD, THCA, ACC and KIRC. The DLBC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .008). Together, the overview and detailed table identify DLBC as the clearest survival context for TERF2IP RNA expression.
This table summarizes TERF2IP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in THCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for TERF2IP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. TERF2IP shows lower tumor expression in THCA, KICH, LUSC, LUAD and UCEC and higher tumor expression in HNSC. The THCA box plot shows higher TERF2IP RNA expression in normal versus tumor tissue (log2 FC = −0.430, t-test p < 0.001).
This table shows molecular features associated with TERF2IP in patient tissues and cancer cell lines. In patient samples, TERF2IP 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, TERF2IP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
