{"doi":"10.1002/ctd2.100","title":"Pan‐cancer analysis of altered glycosyltransferases confers poor clinical outcomes","abstract":"Glycosylation is a post-translational modification (PTM) process that attaches carbohydrates to proteins and lipids. Protein glycosylation can be N-linked or O-linked depending on adding sugars onto the side chains of Asn or Ser/Thr residues, respectively. PTM comprises glycan-modifying enzymes glycosyltransferases (GTs) and glycosidases. O-glycan truncation, N-glycan branching, sialylation and fucosylation are the most well-known cancer-associated glycosylations. The glycan-modifying enzymes stimulate various malignant behaviours of tumours, such as tumour proliferation, invasion, epithelial-mesenchymal transition, metastasis, angiogenesis, immune modulation and cell-matrix interactions.1, 2 Over 200 GTs regulate glycosylation. An unbiased global approach must be used to identify glycogenes in cancer. In this study, Li et al.3 (2022) investigated the fascinating examination of GTs in pan-cancer using bioinformatics investigation from CCLE, TCGA, single-cell RNA sequencing datasets and proteogenomic assets across various cancer types. The study by Li et al.3 uncovered mutations of GTs across 33 types of cancer. Also, the study has interestingly discovered that the top three mutated GTs are ALG13 (11.6%) in uterine corpus endometrial carcinoma (UCEC), FUT9 (10.6%), GALNT13 (10.6%) in skin cutaneous melanoma (SKCM) and UGGT2 (> 5%) in colon adenocarcinoma (COAD). The survival analysis of patients with UGGT2 mutations in COAD was linked to worse clinical outcomes, whereas ALG13 mutations in UCEC were linked with better survival. This finding indicates that GTs mutations play a vital role in the patients' overall survival. Li et al.3 further analyzed the drug sensitivity study from CCLE databases which revealed that the UGGT2 mutation was resistant to EGFR inhibitors (Erlotinib and Lapatinib) in colon cancer cell lines and ALG13 mutation was sensitive to Panobinostat and Sorafenib in endometrial cancer cell lines (Figure 1A). These findings imply that identifying GTs mutations will pave the way for better cancer treatment. The extended study by Li et al. showed expressional changes in GTs in 16 tumors; among them, three GTs (ALG3, B3GALT2 and ST6GALNAC3) displayed consistent alterations across 16 cancers. Interestingly, upregulation of ALG3 and downregulation of B3GALT2 and ST6GALNAC3 were found across cancer types. The functional analysis of these three GTs indicates that the changes in expression and biological effects are functionally similar across different types of cancer. These findings also suggest that biological function influences tumorigenesis in all types of 16 cancers. Furthermore, the expression of GTs is tightly associated with patients' prognoses. The reduced expression of GYS2 conveyed a poor prognosis in liver hepatocellular carcinoma (LIHC). The aberrant expression of B3GNT3 and GALNT14 were related to overall survival in lung adenocarcinoma (LUAD) (Figure 1B). In addition, the study by Li et al. demonstrated the correlation between GTs and tumor microenvironment (TME) in cancer. The expression of MFNG is positively correlated to activated CD8+ T cells in LUAD and SKCM. The high infiltration of active CD8+ T cells indicates a favorable prognosis, indicating that activating these cells in the TME has a therapeutic advantage. The single-cell RNA sequencing dataset revealed that MFNG was expressed in CX3CR1+ cytotoxic T cells.4, 5 Identifying the association between MFNG and CD8+ T cells will lead to a better prognosis in LAUD and SKCM (Figure 2A). In the proteogenomic study, Li et al.3 identified and validated that GALNT4, MGAT5 and UGGT2, were substantially correlated with proliferation. The interaction between the TME components and GTs is significantly associated with angiogenesis, EMT, hypoxia and stromal cells in LIHC. Patients with high MGAT5 and UGGT2 expression had shorter overall survival according to the protein expression study by tissue microarrays (N154). Moreover, the inhibition effect of the GTs (G","journal":"Clinical and Translational Discovery","year":2022,"id":285099,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9569,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":106461,"name":"Surinder K. Batra","orcid":"0000-0001-9470-9317","position":1,"is_corresponding":false},{"id":264866,"name":"Moorthy P. Ponnusamy","orcid":"0000-0001-5744-193X","position":2,"is_corresponding":false},{"id":264856,"name":"Saravanakumar Marimuthu","orcid":"0000-0003-3640-5408","position":0,"is_corresponding":true}],"reference_count":11,"raw_metadata":null,"created_at":"2026-07-19T00:29:40.826853Z","pmid":"35875597","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}