Characterization of cuproptosis in gastric cancer and relationship with clinical and drug reactions

Abstract

Gastric cancer (GC) is the fifth most common cancer worldwide. Cuproptosis is associated with cell growth and death as well as tumorigenesis. Aiming to lucubrate the potential influence of CRGs in gastric cancer, we acquired datasets of gastric cancer patients from TCGA and GEO. The identification of molecular subtypes with CRGs expression was achieved through unsupervised learning-cluster analysis. To evaluate the application value of subtypes, the K-M survival analysis was conducted to evaluate the clinical prognostic characteristics. Subsequently, we performed Gene Set Variation Analysis (GSVA) and utilized ssGSEA to quantify the extent of immune infiltration. Further, the K-M survival analysis was used to identify the prognosis-related CRGs. Next, signature genes of diagnostic predictive value were screened using the least absolute shrinkage and selection operator (LASSO) algorithm from the expression matrix for TCGA, as well as the signature gene-related subtype was clustered by the “ConsensusClusterPlus” package. Finally, the immunological and drug sensitivity assessments of the signature gene-related subtypes were conducted. A total of 173 CRGs were identified, most of the CRGs undergo copy number variation in gastric cancer. Under different patient subtypes, immune cell levels differed significantly, and the subtype exhibiting high expression of the CRGs had a better prognosis. Furthermore, we selected 34 CRGs that were highly correlated with the prognosis of gastric cancer. By constructing a multivariate Cox proportional-hazards model and a hazard scoring system, we were able to categorize patients into high- and low-risk groups based on their hazard score. K-M analysis demonstrated a significant survival disadvantage in the high-risk group. Based on Lasso regression analysis, we screened 16 signature genes, a multivariate logistic regression model [cutoff: 0.149 (0.000, 0.974), AUC:0.987] and a prognosis network diagram was constructed and their prediction efficiency for gastric cancer prognostic diagnosis was well validated. According to the signature genes, the patients were separated to two signature subtypes. We found that patients with higher CRGs expression and better prognosis had lower levels of immune infiltration. Finally, according to the results of drug susceptibility analysis, docetaxel, 5-Fluorouracil, gemcitabin, and paclitaxel were found to be more sensitive to gastric cancer.

Kinesin 26B modulates M2 polarization of macrophage by activating cancer-associated fibroblasts to aggravate gastric cancer occurrence and metastasis

“…GC is one of the most severe cancers globally[ 28 , 29 ]. Exploring GC progression mechanisms and developing new treatments have become necessary.…”

Clinical features and molecular landscape of cuproptosis signature‐related molecular subtype in gastric cancer

“…The utilization of multi‐omics technology has enabled the identification of cancer biomarkers generated by tumor cells within the intricate tumor microenvironment, thereby significantly advancing the development of novel diagnostic and therapeutic approaches [ 36 ]. There have been studies constructing prognosis models based on copper death features, but they have not comprehensively considered clinical features, and the explanation of the genetic molecular landscape is not in‐depth enough [ 37 ]. There is no cross‐validation between drug screening and cell line sensitivity, and no sensitivity‐related experiments have been conducted to verify the screened drugs [ 38 ].…”