Q-omics provides the consensus-scored SLC16A7 profile across patient tissues and cancer cell-line models. SLC16A7 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, SLC16A7 is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, SLC16A7 protein abundance shows 21,723 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight STAD, KIRC, and GBM as cancer lineages where SLC16A7 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 SLC16A7 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SLC16A7 survival associations across molecular data types. SLC16A7 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (2) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SLC16A7 RNA expression–survival associations across cancer types. High SLC16A7 expression shows unfavorable associations in STAD, BLCA, CESC and UVM, but favorable associations in ACC and BRCA. The STAD 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 STAD as the clearest survival context for SLC16A7 RNA expression.
This table summarizes SLC16A7 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 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SLC16A7. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SLC16A7 shows lower tumor expression in KIRC, HNSC, THCA, BRCA and UCEC and higher tumor expression in LUAD. The KIRC box plot shows higher SLC16A7 RNA expression in normal versus tumor tissue (log2 FC = −2.448, t-test p < 0.001).
This table shows molecular features associated with SLC16A7 in patient tissues and cancer cell lines. In patient samples, SLC16A7 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, SLC16A7 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 OESOPHAGUS and BLOOD_Leukemia.
