From the standpoint of the ER (endoplasmic reticulum), we were interested in identifying hub genes that impact clinical prognosis for HCC (hepatocellular carcinoma) patients and developing an ER-related prognostic model. Using TCGA-LIHC (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) and GSE14520 datasets, we conducted a series of analyses, which included differential gene screening, clinical prognostic analysis, Lasso regression, nomogram prediction, tumour clustering, gene functional enrichment, and tumour infiltration of immune cells. Following our screening for ER-related genes ( n = 1975 ), we conducted a Lasso regression model to obtain five hub genes, KPNA2, FMO3, SPP1, KIF2C, and LPCAT1, using TCGA-LIHC as a training set. According to risk scores, HCC samples within either the TCGG-LIHC or GSE14520 cohort were categorized into high- and low-risk groups. Compared to the high-risk group of HCC patients, patients in the low-risk group had a better prognosis of OS (overall survival) or RFS (relapse-free survival). For TCGA-LIHC training set, with the factors of risk score, stage, age, and sex, we plotted a nomogram for 1-, 3-, and 5-year survival predictions. Our model demonstrated better clinical validity in both TCGA-LIHC and GSE14520 cohorts. Additionally, events related to biological enzyme activity, biological metabolic processes, or the cell cycle were associated with the prognostic risk of ER. Furthermore, two HCC prognosis-associated tumour clusters were identified by ER hub gene-based consensus clustering. Our findings indicated a link between ER prognostic signature-related high/low risk and tumour infiltration levels of several immune cells, such as “macrophages M2/M0” and “regulatory T cells (Tregs).” Overall, we developed a novel ER-related clinical prognostic model for HCC patients.

Background Lung squamous cell carcinoma (LUSC) is an important subtype of non-small cell lung cancer. Its special clinicopathological features and molecular background determine the limitations of its treatment. A recent study published on Science defined a newly regulatory cell death (RCD) form – cuproptosis. Which manifested as an excessive intracellular copper accumulation, mitochondrial respiration-dependent, protein acylation-mediated cell death. Different from apoptosis, pyroptosis, necroptosis, ferroptosis and other forms of regulatory cell death (RCD). The imbalance of copper homeostasis in vivo will trigger cytotoxicity and further affect the occurrence and progression of tumors. Our study is the first to predict the prognosis and immune landscape of cuproptosis-related genes (CRGs) in LUSC. Methods The RNA-seq profiles and clinical data of LUSC patients were downloaded from TCGA and GEO databases and then combined into a novel cohort. R language packages are used to analyze and process the data, and CRGs related to the prognosis of LUSC were screened according to the differentially expressed genes (DEGs). After analyzed the tumor mutation burden (TMB), copy number variation (CNV) and CRGs interaction network. Based on CRGs and DEGs, cluster analysis was used to classify LUSC patients twice. The selected key genes were used to construct a CRGs prognostic model to further analyze the correlation between LUSC immune cell infiltration and immunity. Through the risk score and clinical factors, a more accurate nomogram was further constructed. Finally, the drug sensitivity of CRGs in LUSC was analyzed. Results Patients with LUSC were divided into different cuproptosis subtypes and gene clusters, showing different levels of immune infiltration. The risk score showed that the high-risk group had higher tumor microenvironment score, lower tumor mutation load frequency and worse prognosis than the low-risk group. In addition, the high-risk group was more sensitive to vinorelbine, cisplatin, paclitaxel, doxorubicin, etoposide and other drugs. Conclusions Through bioinformatics analysis, we successfully constructed a prognostic risk assessment model based on CRGs, which can not only accurately predict the prognosis of LUSC patients, but also evaluate the patient 's immune infiltration status and sensitivity to chemotherapy drugs. This model shows satisfactory predictive results and provides a reference for subsequent tumor immunotherapy.