Journal of Cancer最新文献

Background: Studies on immunogenic death (ICD) in lung adenocarcinoma are limited, and this study aimed to determine the function of ICD in LUAD and to construct a novel ICD-based prognostic model to improve immune efficacy in lung adenocarcinoma patients. Methods: The data for lung adenocarcinoma were obtained from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). The single-cell data were obtained from Bischoff P et al. To identify subpopulations, we performed descending clustering using TSNE. We collected sets of genes related to immunogenic death from the literature and identified ICD-related genes through gene set analysis of variance (GSVA) and weighted gene correlation network analysis (WGCNA). Lung adenocarcinoma patients were classified into two types using consistency clustering. The difference between the two types was analyzed to obtain differential genes. An immunogenic death model (ICDRS) was established using LASSO-Cox analysis and compared with lung adenocarcinoma models of other individuals. External validation was performed in the GSE31210 and GSE50081 cohorts. The efficacy of immunotherapy was assessed using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and immune microenvironment between different risk groups were investigated. Subsequently, ROC diagnostic curves and KM survival curves were used to screen ICDRS key regulatory genes. Finally, RT-qPCR was used to verify the differential expression of these genes. Results: Eight ICD genes were found to be highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. Additionally, ICDRS demonstrated better predictive ability compared to 11 previously published models. Furthermore, significant differences in biological function and immune cell infiltration were observed in the tumor microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both groups. These findings suggest that the model has good predictive efficacy. Conclusions: The ICD model demonstrated good predictive performance, revealing the tumor microenvironment and providing a new method for evaluating the efficacy of pre-immunization. This offers a new strategy for future treatment of lung adenocarcinoma.

Objective : To explore the roles of Neural precursor cell expressed developmentally down-regulated 1(NEDD1) in lung cancer tumorigenesis and the relationship between NEDD1 expression and clinicopathology of patients with lung adenocarcinoma (LUAD). Methods: Expression of NEDD1 or other proteins in tissues and cell lines were determined with immunohistochemistry or western blot, the data of patients with LUAD in The Cancer Genome Atlas (TCGA) datasets and LUAD tissue array were collected and analyzed, the effects of NEDD1 on proliferation, migration, cell cycle progression and apoptosis of cancer cells were detected with colony formation assay, transwell assay and Flow cytometry (FCM) analysis respectively. the impact of NEDD1 knockdown on DNA damage was analyzed using Immunofluorescence staining of H2AX and comet assay. Furthermore, the effect of NEDD1 on cancer cell proliferation in vivo was investigated in nude mice. Results : NEDD1 was upregulated in lung tissues and the NEDD1 immune score was an independent prognostic factor. Overexpression of NEDD1 promoted epithelial-mesenchymal transition, accelerated cell cycle progression, and enhanced the proliferation and migration of A549 and H1299 cells, while knockdown of NEDD1 resulted in the opposite phenotype and leaded to DNA damage. In addition, NEDD1 improved cell tumorigenicity in vivo. Conclusion : These findings suggest that NEDD1 plays important roles in lung cancer development and may therefore be a potential prognostic marker and promising therapeutic target for lung cancer therapy.

[This corrects the article DOI: 10.7150/jca.57477.].

Skin cutaneous melanoma (SKCM), a malignant melanocyte-derived skin cancer, potentially leads to fatal outcomes without effective treatment. The variability in immunotherapy responses among melanoma patients is significantly influenced by the intricate immune microenvironment, particularly due to the status of tumor T cells, encompassing their activity, exhaustion levels, and antigen recognition capabilities. This study utilized single-cell RNA sequencing (scRNA-seq) to analyze 34 melanoma samples from two public datasets (GSE215120 and GSE115978). Herein, we extracted 706 marker genes associated with immune checkpoint (ICP) therapy from these T cells, 509 markers of T cells from 11 melanoma tissues, and eventually identified 33 candidate genes. These genes underwent LASSO and COX regression analyses to identify the signature genes. Of the initial 33 candidate genes, we successfully isolated six distinct T cell-associated immunotherapy-related genes (IRTGs). Additionally, the computation of each patient risk score proved beneficial in evaluating the immune cell infiltration level and functions as an independent prognostic factor for melanoma patient survival. The risk score results revealed promising predictive outcomes in determining the response of melanoma patients to immunotherapy. Notably, our study is the first to reveal the potential correlation between signature gene PEB4B and the immune microenvironment in melaoma, which was explored with multiple immunofluorescence (IF) and Immune Infiltration Assessment. In a conclusion, our findings demonstrate the potential utility of a risk score dependent on signature genes as a predictive tool for assessing the prognosis and response to immunotherapeutic interventions in melanoma patients.

Background: The pervasive role of alternative promoters in context-specific isoform expression and the importance of promoter choice over its level of transcriptional activity have been recently implied based on pan-cancer in silico studies. We aimed to explore this phenomenon at the cellular level on the example of a major tumor suppressor SMAD4 in search of molecular mechanisms in colorectal cancer that could be exploited for novel biomarkers or therapeutic approaches. Methods: Multi-omics technologies, in silico tools and in vitro functional assays were applied to analyze the transcripts expression and the alternative promoters' function of the SMAD4 gene in colon cell lines HCEC-1CT, HCT116, DLD-1, SW480 and SW620. Results: High expression of the transcript SMAD4-213 emerged as a hallmark of colon cancer cells, while in silico tools point to its possible additional role and potential for sponging miRNAs. Based on the observed dysregulation of SMAD4-209 and SMAD4-213 in malignant vs. non-malignant colon cells, we propose that their expression ratio might be a solid biomarker candidate for colorectal cancer detection. Conclusions: A differential pattern of the respective promoters' activity was observed that corresponds to the expression of transcripts, confirming the role of alternative promoters in context-specific isoform expression. The investigated SMAD4 promoters and transcripts harbor translational potential that should be further investigated.

ELF4 (E74-like factor 4) is a transcription factor, dysregulation of which has been associated with carcinogenesis and cancer development. Nevertheless, the precise role of ELF4 in glioma pathology and its impact on clinical outcomes remains to be investigated. In the present research, comprehensive analyses demonstrated that elevated expression of ELF4 in glioma tissues correlates with malignant phenotypes and adverse clinical outcomes. Multivariate Cox regression analysis determined that ELF4 expression could serve as a reliable predictor of glioma outcomes. (CGGA, hazard ratio [HR]: 1.21, 95% confidence interval [CI]: 1.09-1.34, p<0.001; TCGA, HR: 1.19, 95%CI: 1.01-1.41, p=0.043; and Gravendeel, HR: 1.44, 95%CI: 1.15-1.80, p=0.002). Knockdown of ELF4 reduced the cell viability and migration capacity of glioma cells in vitro. In addition to the tumor invasive role, enrichment analysis revealed the overexpressed ELF4 was involved in the immune regulation, characterized by the elevated activity of Il6/Jak/Stat3 signaling, interferon alpha (IFN-α) response, and IL2/Stat5 signaling. Single-cell RNA sequencing (scRNA)-seq and spatial transcriptome (ST)-seq analyses revealed that ELF4 could induce reprogramming of tumor-associated monocytes/macrophages (TAMMs). Molecular docking analysis revealed ELF4 might be targeted by drugs/compounds, including Veliparib (ABT-888), Motesanib (AMG 706), and EHT 1864. Genomic analysis revealed that, in LGG, in the low ELF4 expression subgroup, IDH1 demonstrated a higher mutation rate, and TP53 and ATRX Chromatin Remodeler (ATRX) displayed the lower mutation rates, than the high ELF4 expression group. Conclusion: Our research suggests that ELF4 may contribute to the prognostic assessment of glioma and personalized medicine.

Mitochondrial oxidative phosphorylation (OXPHOS) has long been considered the primary energy source in breast cancer cells. Cytochrome c oxidase assembly factor 6 (COA6), which functions as a metal chaperone to transport copper to complex Ⅳ during the OXPHOS process, plays a crucial role in the carcinogenesis of lung adenocarcinoma. Nevertheless, its specific function in breast cancer is undefined. The present investigation aimed to clarify COA6's expression profile and regulatory functions in breast cancer, as well as to unveil its underlying mechanisms. Initially, our findings revealed a significant upregulation of COA6 in breast cancer, as evidenced by an analysis of the TCGA database and tissue microarrays. This upregulation correlated with tumor size and histological grade. Additionally, survival analysis revealed that elevated COA6 amounts were correlated with decreased overall survival (OS) in breast cancer. To delve deeper into the functions of COA6, both COA6-overexpressing and COA6-knockdown breast cancer cell models were established. These experiments demonstrated COA6 is pivotal in regulating cell proliferation, apoptosis, migration, and invasion, thereby promoting cancer progression in vitro. Notably, functional enrichment analysis indicated COA6 might be involved in breast cancer progression by modulating oxidative phosphorylation (OXPHOS). Collectively, this study reveals an overt tumorigenic role for COA6 in breast cancer and sheds light on its potential mechanisms, offering valuable therapeutic targets for breast cancer therapy.

Objective: This study aims to develop an interpretable machine learning (ML) model to accurately predict the probability of achieving total pathological complete response (tpCR) in patients with locally advanced breast cancer (LABC) following neoadjuvant chemotherapy (NAC). Methods: This multi-center retrospective study included pre-NAC clinical pathology data from 698 LABC patients. Post-operative pathological outcomes divided patients into tpCR and non-tpCR groups. Data from 586 patients at Shanghai Ruijin Hospital were randomly assigned to a training set (80%) and a test set (20%). In comparison, data from our hospital's remaining 112 patients were used for external validation. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Predictive models were constructed using six ML algorithms: decision trees, K-nearest neighbors (KNN), support vector machine, light gradient boosting machine, and extreme gradient boosting. Model efficacy was assessed through various metrics, including receiver operating characteristic (ROC) curves, precision-recall (PR) curves, confusion matrices, calibration plots, and decision curve analysis (DCA). The best-performing model was selected by comparing the performance of different algorithms. Moreover, variable relevance was ranked using the SHapley Additive exPlanations (SHAP) technique to improve the interpretability of the model and solve the "black box" problem. Results: A total of 191 patients (32.59%) achieved tpCR following NAC. Through LASSO regression analysis, five variables were identified as predictive factors for model construction, including tumor size, Ki-67, molecular subtype, targeted therapy, and chemotherapy regimen. The KNN model outperformed the other five classifier algorithms, achieving area under the curve (AUC) values of 0.847 (95% CI: 0.809-0.883) in the training set, 0.763 (95% CI: 0.670-0.856) in the test set, and 0.665 (95% CI: 0.555-0.776) in the external validation set. DCA demonstrated that the KNN model yielded the highest net advantage through a wide range of threshold probabilities in both the training and test sets. Furthermore, the analysis of the KNN model utilizing SHAP technology demonstrated that targeted therapy is the most crucial factor in predicting tpCR. Conclusion: An ML prediction model using clinical and pathological data collected before NAC was developed and verified. This model accurately predicted the probability of achieving a tpCR in patients with LABC after receiving NAC. SHAP technology enhanced the interpretability of the model and assisted in clinical decision-making and therapy optimization.

Colorectal cancer (CRC) is the third most frequent cancer worldwide and the second major cause of cancer-related death. Thus, we attempted to ascertain the relationship between the genotype and allele frequencies of phosphatase and tensin homolog (PTEN) and immunohistochemical PTEN expression with clinicopathological characteristics in patients with CRC. 150 individuals were allocated into two groups for this cross-sectional randomized case-control study: Group I consisted of 100 patients with histopathologically proven CRC of various stages. Group II: Fifty healthy volunteers. Genetic analysis of PTEN (rs701848 T / C) single nucleotide polymorphism (SNP) was performed using TaqMan^TM assays and real-time PCR, while PTEN expressions were assessed using immunohistochemical staining. PTN SNP genotypes and alleles did not significantly differ between CRC patients and controls. PTEN expression was lost in 28% of CRC patients, while all healthy controls exhibited PTEN expression. Negative PTEN expression was present in 16 (80%) of stage IV CRC cases, 9 (23.7%) of stage III cases, 3 (37.5%) of stage II cases, and none of stage I cases. It was shown that PTEN expression was weakly positive, moderately positive, and strongly positive in 15, 10, and 9 (respectively) cases of CRC stage I. However, the expression was only weekly positive in 4 (20%) of the patients in stage IV. In the stage IV group, neither moderately nor strongly positive PTEN expressions were found. So, Among Egyptians, the emergence or course of colorectal cancer is unrelated to the PTEN gene mutation. However, the formation and progression of CRC may be influenced by weak or lost PTEN expression.

Hepatocellular carcinoma (HCC) is a highly lethal cancer with a growing global incidence and is often associated with poor prognosis due to its tendency to metastasize. Intercellular adhesion molecule (ICAM) 1 is a transmembrane protein found in various cancer cells and is associated with the spread of cancer and poor prognosis. Chemokine (C-X-C motif) ligand 1 (CXCL1) is a chemokine that significantly affects the cell motility of various cancers. However, the role of CXCL1 in ICAM-1 expression and in metastasis of hepatocellular carcinoma remains unclear. We determined that CXCL1 expression is positively and significantly associated with advanced-stage tumors in the HCC tissue array. Kaplan-Meier analysis revealed worse overall survival rates in the high CXCL1 expression group, suggesting its potential as a biomarker for cancer progression and stimulating hepatocellular carcinoma cells with CXCL1 enhanced migration abilities by upregulating ICAM-1 expression. CXCL1 was shown to enhance ICAM-1-dependent cell motility by inhibiting miR-30b-5p. This study provides novel evidence that CXCL1 could serve as a therapeutic target for metastasis in hepatocellular carcinoma.