Q-omics provides the consensus-scored SGSM1 profile across patient tissues and cancer cell-line models. SGSM1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SGSM1 is differentially expressed in 14, with the highest sampling consensus in KIRP. Additionally, SGSM1 RNA expression shows 20,001 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, KIRP, and UVM as cancer lineages where SGSM1 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 SGSM1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SGSM1 survival associations across molecular data types. SGSM1 RNA expression shows survival associations in the most cancer types (26), 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 SGSM1 RNA expression–survival associations across cancer types. High SGSM1 expression shows unfavorable associations in UVM, UCEC and ACC, but favorable associations in KIRC, MESO and LGG. The KIRC 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 KIRC as the clearest survival context for SGSM1 RNA expression.
This table summarizes SGSM1 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 4. The strongest signals are observed in KIRP for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for SGSM1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SGSM1 shows lower tumor expression in KIRP, HNSC, COAD, LUSC and UCEC and higher tumor expression in LIHC. The KIRP box plot shows higher SGSM1 RNA expression in normal versus tumor tissue (log2 FC = −1.355, t-test p < 0.001).
This table shows molecular features associated with SGSM1 in patient tissues and cancer cell lines. In patient samples, SGSM1 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, SGSM1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BLOOD_Lymphoma.
