Q-omics provides the consensus-scored SPHK1 profile across patient tissues and cancer cell-line models. SPHK1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SPHK1 is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, SPHK1 RNA expression shows 19,450 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight KIRC, COAD, and LUAD as cancer lineages where SPHK1 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 SPHK1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SPHK1 survival associations across molecular data types. SPHK1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) 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 SPHK1 RNA expression–survival associations across cancer types. High SPHK1 expression shows unfavorable associations in KIRC, UVM, ACC, UCS, MESO and BLCA. The KIRC 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 KIRC as the clearest survival context for SPHK1 RNA expression.
This table summarizes SPHK1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SPHK1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SPHK1 shows higher tumor expression in COAD, HNSC, THCA, LIHC, KIRP and STAD. The COAD box plot shows higher SPHK1 RNA expression in tumor versus normal tissue (log2 FC = +2.178, t-test p < 0.001).
This table shows molecular features associated with SPHK1 in patient tissues and cancer cell lines. In patient samples, SPHK1 shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, SPHK1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BONE.
