Q-omics provides the consensus-scored SLIRP profile across patient tissues and cancer cell-line models. SLIRP expression is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, SLIRP is differentially expressed in 12, with the highest sampling consensus in LIHC. Additionally, SLIRP protein abundance shows 27,832 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, LIHC, and LSCC as cancer lineages where SLIRP 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 SLIRP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SLIRP survival associations across molecular data types. SLIRP RNA expression shows survival associations in the most cancer types (30), followed by mutation status (1) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SLIRP RNA expression–survival associations across cancer types. High SLIRP expression shows unfavorable associations in UVM, KICH, HNSC, LUAD, ACC and UCS. The UVM 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 UVM as the clearest survival context for SLIRP RNA expression.
This table summarizes SLIRP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SLIRP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SLIRP shows higher tumor expression in LIHC, HNSC, BLCA, LUSC, LUAD and UCEC. The LIHC box plot shows higher SLIRP RNA expression in tumor versus normal tissue (log2 FC = +0.903, t-test p < 0.001).
This table shows molecular features associated with SLIRP in patient tissues and cancer cell lines. In patient samples, SLIRP shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, SLIRP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
