Q-omics provides the consensus-scored SARNP profile across patient tissues and cancer cell-line models. SARNP expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SARNP is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, SARNP protein abundance shows 19,867 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, LIHC, and GBM as cancer lineages where SARNP 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 SARNP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SARNP survival associations across molecular data types. SARNP RNA expression shows survival associations in the most cancer types (24), followed by mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SARNP RNA expression–survival associations across cancer types. High SARNP expression shows unfavorable associations in ACC, KICH, UVM, KIRC and LIHC, but favorable associations in UCS. The ACC 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 ACC as the clearest survival context for SARNP RNA expression.
This table summarizes SARNP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 4. The strongest signals are observed in LIHC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SARNP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SARNP shows lower tumor expression in KICH and higher tumor expression in LIHC, HNSC, KIRC, READ and STAD. The LIHC box plot shows higher SARNP RNA expression in tumor versus normal tissue (log2 FC = +0.639, t-test p < 0.001).
This table shows molecular features associated with SARNP in patient tissues and cancer cell lines. In patient samples, SARNP shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, SARNP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in LIVER and UPPER_AERODIGESTIVE_TRACT.
