Q-omics provides the consensus-scored SPHK2 profile across patient tissues and cancer cell-line models. SPHK2 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, SPHK2 is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, SPHK2 RNA expression shows 19,437 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight HNSC, KIRC, and ACC as cancer lineages where SPHK2 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 SPHK2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SPHK2 survival associations across molecular data types. SPHK2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SPHK2 RNA expression–survival associations across cancer types. High SPHK2 expression shows unfavorable associations in ACC, UVM, SKCM, LGG and MESO, but favorable associations in HNSC. The HNSC 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 HNSC as the clearest survival context for SPHK2 RNA expression.
This table summarizes SPHK2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SPHK2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SPHK2 shows lower tumor expression in KIRC, THCA, KICH and KIRP and higher tumor expression in BRCA and HNSC. The KIRC box plot shows higher SPHK2 RNA expression in normal versus tumor tissue (log2 FC = −1.051, t-test p < 0.001).
This table shows molecular features associated with SPHK2 in patient tissues and cancer cell lines. In patient samples, SPHK2 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, SPHK2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Lymphoma.
