Q-omics provides the consensus-scored TBC1D16 profile across patient tissues and cancer cell-line models. TBC1D16 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, TBC1D16 is differentially expressed in 16, with the highest sampling consensus in COAD. Additionally, TBC1D16 RNA expression shows 19,533 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight SKCM, COAD, and ACC as cancer lineages where TBC1D16 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 TBC1D16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TBC1D16 survival associations across molecular data types. TBC1D16 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) 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 TBC1D16 RNA expression–survival associations across cancer types. High TBC1D16 expression shows unfavorable associations in SKCM, BLCA, UVM, ACC, LIHC and LGG. The SKCM 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 SKCM as the clearest survival context for TBC1D16 RNA expression.
This table summarizes TBC1D16 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 5. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for TBC1D16. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. TBC1D16 shows lower tumor expression in THCA and higher tumor expression in COAD, LIHC, HNSC, STAD and LUAD. The COAD box plot shows higher TBC1D16 RNA expression in tumor versus normal tissue (log2 FC = +1.432, t-test p < 0.001).
This table shows molecular features associated with TBC1D16 in patient tissues and cancer cell lines. In patient samples, TBC1D16 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, TBC1D16 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 BLOOD_Leukemia and SKIN.
