Q-omics provides the consensus-scored TERF2 profile across patient tissues and cancer cell-line models. TERF2 expression is associated with patient survival in 17 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, TERF2 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, TERF2 protein abundance shows 20,837 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, HNSC, and GBM as cancer lineages where TERF2 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 TERF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TERF2 survival associations across molecular data types. TERF2 RNA expression shows survival associations in the most cancer types (17), followed by mutation status (5) 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 TERF2 RNA expression–survival associations across cancer types. High TERF2 expression shows unfavorable associations in ACC, LAML, LIHC and LUSC, but favorable associations in KIRC and SCLC. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for TERF2 RNA expression.
This table summarizes TERF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, 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 TERF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. TERF2 shows lower tumor expression in KICH and THCA and higher tumor expression in HNSC, LIHC, LUAD and KIRP. The HNSC box plot shows higher TERF2 RNA expression in tumor versus normal tissue (log2 FC = +0.662, t-test p < 0.001).
This table shows molecular features associated with TERF2 in patient tissues and cancer cell lines. In patient samples, TERF2 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, TERF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in CNS and UPPER_AERODIGESTIVE_TRACT.
