Q-omics provides the consensus-scored SLX4IP profile across patient tissues and cancer cell-line models. SLX4IP expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, SLX4IP is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, SLX4IP RNA expression shows 20,760 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KICH, HNSC, and UVM as cancer lineages where SLX4IP 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 SLX4IP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SLX4IP survival associations across molecular data types. SLX4IP RNA expression shows survival associations in the most cancer types (25), followed by mutation status (2) 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 SLX4IP RNA expression–survival associations across cancer types. High SLX4IP expression shows unfavorable associations in KICH, LGG and MESO, but favorable associations in READ, PAAD and KIRC. The KICH Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .004). Together, the overview and detailed table identify KICH as the clearest survival context for SLX4IP RNA expression.
This table summarizes SLX4IP 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 2. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for SLX4IP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SLX4IP shows higher tumor expression in HNSC, BLCA, STAD, KIRP, LUAD and COAD. The HNSC box plot shows higher SLX4IP RNA expression in tumor versus normal tissue (log2 FC = +1.087, t-test p < 0.001).
This table shows molecular features associated with SLX4IP in patient tissues and cancer cell lines. In patient samples, SLX4IP 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, SLX4IP 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 BLOOD_Myeloma and BLOOD_Leukemia.
