Q-omics provides the consensus-scored SGK2 profile across patient tissues and cancer cell-line models. SGK2 expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, SGK2 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, SGK2 RNA expression shows 19,044 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight BLCA, KIRC, and UVM as cancer lineages where SGK2 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 SGK2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SGK2 survival associations across molecular data types. SGK2 RNA expression shows survival associations in the most cancer types (19), followed by mutation status (3) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SGK2 RNA expression–survival associations across cancer types. High SGK2 expression shows unfavorable associations in PRAD and LUSC, but favorable associations in BLCA, HNSC, SCLC and CESC. The BLCA 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 BLCA as the clearest survival context for SGK2 RNA expression.
This table summarizes SGK2 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 3. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SGK2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SGK2 shows lower tumor expression in KIRC, COAD, BRCA and KIRP and higher tumor expression in LUAD and LIHC. The KIRC box plot shows higher SGK2 RNA expression in normal versus tumor tissue (log2 FC = −1.902, t-test p < 0.001).
This table shows molecular features associated with SGK2 in patient tissues and cancer cell lines. In patient samples, SGK2 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, SGK2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Leukemia.
