Comprehensive analyses of solute carrier family members identify SLC12A2 as a novel therapy target for colorectal cancer.

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We aimed to identify a prognostic signature from the SLC members and comprehensively analyze their roles in CRC. Firstly, we downloaded transcriptome data and clinical information of CRC samples from GEO (GSE39582) and TCGA as training and testing dataset, respectively. We extracted the expression matrix of SLC genes and established a prognostic model by univariate and multivariate Cox regression. Afterwards, the low-risk and high-risk group were identified. Then, the differences of prognosis traits, transcriptome features, clinical characteristics, immune infiltration and drug sensitivity between the two groups were explored. Furthermore, molecular subtyping was also implemented by non-negative matrix factorization (NMF). Finally, we studied the expression of the screened SLC genes in CRC tumor tissues and normal tissues as well as investigated the role of SLC12A2 by loss of function and gain of function. As a result, we developed a prognostic risk model based on the screened 6-SLC genes (SLC39A8, SLC2A3, SLC39A13, SLC35B1, SLC4A3, SLC12A2). Both in the training and testing sets, CRC patients in the high-risk group had the poorer prognosis and were in the more advanced pathological stage. What's more, the high-risk group were enriched with CRC progression signatures and immune infiltration. Two groups showed different drug sensitivity. On the other hand, two distinct subclasses (C1 and C2) were identified based on the 6 SLC genes. CRC patients in the high-risk group and C1 subtype had a worse prognosis. Furthermore, we found and validated that SLC12A2 was steadily upregulated in CRC. A loss-of-function study showed that knockdown of SLC12A2 expression restrained proliferation and stemness of CRC cells while a gain-of-function study showed the contrary results. Hence, we provided a 6-SLC gene signature for prognosis prediction of CRC patients. At the same time, we identified that SLC12A2 could promote tumor progression in CRC, which may serve as a potential therapeutic target.", "claims": [{"id": "claim-method", "statement": "Initially, univariate Cox regression analysis was conducted using the \"survival\" R package in both GSE39582 and TCGA COAD datasets. Subsequently, GSE39583 was designated as the training set while TCGA COAD served as the test set for further analysis. Multivariate Cox regression analysis was then performed to determine the regression coefficients associated with prognosis for SLC genes across both datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38396064", "locator": "sections[4] \"Construction of the prognosis-related SLC gene signature\" paragraph 2", "quote": "Initially, univariate Cox regression analysis was conducted using the \"survival\" R package in both GSE39582 and TCGA COAD datasets."}, {"asset": "source:pmid:38396064", "locator": "sections[4] \"Construction of the prognosis-related SLC gene signature\" paragraph 2", "quote": "Subsequently, GSE39583 was designated as the training set while TCGA COAD served as the test set for further analysis."}, {"asset": "source:pmid:38396064", "locator": "sections[4] \"Construction of the prognosis-related SLC gene signature\" paragraph 2", "quote": "Multivariate Cox regression analysis was then performed to determine the regression coefficients associated with prognosis for SLC genes across both datasets."}], "state": "complete"}, {"id": "claim-target-result", "statement": "As a result, we developed a prognostic risk model based on the screened 6-SLC genes (SLC39A8, SLC2A3, SLC39A13, SLC35B1, SLC4A3, SLC12A2).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38396064", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "As a result, we developed a prognostic risk model based on the screened 6-SLC genes (SLC39A8, SLC2A3, SLC39A13, SLC35B1, SLC4A3, SLC12A2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "As a result, we developed a prognostic risk model based on the screened 6-SLC genes (SLC39A8, SLC2A3, SLC39A13, SLC35B1, SLC4A3, SLC12A2).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38396064"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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