Q-omics provides the consensus-scored EIF2S2 profile across patient tissues and cancer cell-line models. EIF2S2 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, EIF2S2 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, EIF2S2 protein abundance shows 32,283 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight KIRP, HNSC, and LUAD as cancer lineages where EIF2S2 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 EIF2S2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF2S2 survival associations across molecular data types. EIF2S2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (2) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF2S2 RNA expression–survival associations across cancer types. High EIF2S2 expression shows unfavorable associations in KIRP, LIHC, UVM, ACC, PAAD and MESO. The KIRP 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 KIRP as the clearest survival context for EIF2S2 RNA expression.
This table summarizes EIF2S2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 11. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF2S2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF2S2 shows higher tumor expression in HNSC, COAD, BLCA, KIRC, LIHC and STAD. The HNSC box plot shows higher EIF2S2 RNA expression in tumor versus normal tissue (log2 FC = +1.093, t-test p < 0.001).
This table shows molecular features associated with EIF2S2 in patient tissues and cancer cell lines. In patient samples, EIF2S2 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, EIF2S2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
