Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis

Mechanisms associated with cuproptosis and implications for ovarian cancer

Ovarian cancer, a profoundly fatal gynecologic neoplasm, exerts a substantial economic strain on nations globally. The formidable challenge of its frequent relapse necessitates the exploration of novel cytotoxic agents, efficacious antineoplastic medications with minimal adverse effects, and strategies to surmount resistance to primary chemotherapeutic agents. These endeavors aim to supplement extant pharmacological interventions and elucidate molecular mechanisms underlying induced cytotoxicity, distinct from conventional therapeutic modalities. Recent scientific research has unveiled a novel form of cellular demise, known as copper-death, which is contingent upon the intracellular concentration of copper. Diverging from conventional mechanisms of cellular demise, copper-death exhibits a pronounced reliance on mitochondrial respiration, particularly the tricarboxylic acid (TCA) cycle. Tumor cells manifest distinctive metabolic profiles and elevated copper levels in comparison to their normal counterparts. The advent of copper-death presents alluring possibilities for targeted therapeutic interventions within the realm of cancer treatment. Hence, the primary objective of this review is to present an overview of the proteins and intricate mechanisms associated with copper-induced cell death, while providing a comprehensive summary of the knowledge acquired regarding potential therapeutic approaches for ovarian cancer. These findings will serve as valuable references to facilitate the advancement of customized therapeutic interventions for ovarian cancer.

The role of cuproptosis-related genes in pan-cancer and the development of cuproptosis-related risk model in colon adenocarcinoma

Cancer is widely regarded as a leading cause of death in humans, with colon adenocarcinoma (COAD) ranking among the most prevalent types. Cuproptosis is a novel form of cell death mediated by protein lipoylation. Cuproptosis-related genes (CRGs) participate in tumourigenesis and development. Their role in pan-cancer and COAD require further investigation. This study comprehensively evaluated the relationship among CRGs, pan-cancer, and COAD. Our research revealed the differential expression of CRGs and the cuproptosis potential index (CPI) between normal and tumour tissues, and further explored the correlation of CRGs or CPI with prognosis, immune infiltration, tumor mutant burden(TMB), microsatellite instability (MSI), and drug sensitivity in pan-cancer. Gene set enrichment analysis (GSEA) revealed that oxidative phosphorylation and fatty acid metabolism pathways were significantly enriched in the high CPI group of most tumours. FDX1 and CDKN2A were chosen for further exploration, and we found an independent association between FDX1 and CDKN2A and prognosis, immune infiltration, TMB, and MSI in pan-cancer. Furthermore, a prognostic risk model based on the association between CRGs and COAD was built, and the correlations between the risk score and prognosis, immune-related characteristics, and drug sensitivity were analysed. COAD was then divided into three subtypes using cluster analysis, and the differences among the subtypes in prognosis, CPI, immune-related characteristics, and drug sensitivity were determined. Due to the level of LIPT1 was notably positive related with the risk score, the cytological identification was carried out to identify the association of LIPT1 with proliferation and migration of colon cancer cells. In summary, CRGs can be used as potential prognostic biomarkers to predict immune infiltration levels in patients with pan-cancer. In addition, the risk model could more accurately predict the prognosis and immune infiltration levels of COAD and better guide the direction of clinical medication. Thus, FDX1, CDKN2A, and LIPT1 may serve as prospective new targets for cancer therapy.

Association between strenuous sports or other exercises and lung cancer risk: a mendelian randomization study

Background

Studying the relationship between strenuous sports or other exercises (SSOE) and lung cancer risk remains underexplored. Traditional observational studies face challenges like confounders and inverse causation. However, Mendelian randomization (MR) provides a promising approach in epidemiology and genetics, using genetic variants as instrumental variables to investigate causal relationships. By leveraging MR, we have scrutinized the causal link between SSOE and lung cancer development.

Methods

Twelve single-nucleotide polymorphisms (SNPs) associated with SSOE, as identified in previously published genome-wide association studies, were utilized as instrumental variables in our investigation. Summary genetic data at the individual level were obtained from relevant studies and cancer consortia. The study encompassed a total of 11,348 cases and 15,861 controls. The statistical technique of inverse variance-weighting (IVW), commonly employed in meta-analyses and MR studies, was employed to assess the causal relationship between SSOE and lung cancer risk.

Results

The MR risk analysis indicated a causal relationship between SSOE and the incidence of lung cancer, with evidence of a reduced risk for overall lung cancer [odds ratio (OR) =0.129; 95% confidence interval (CI): 0.021-0.779; P=0.03], lung adenocarcinoma (OR =0.161; 95% CI: 0.012-2.102; P=0.16) and squamous cell lung cancer (OR =0.045; 95% CI: 0.003-0.677; P=0.03). The combined OR for lung cancer from SSOE (controlling for waist circumference and smoking status) was 0.054 (95% CI: 0.010-0.302, P<0.001).

Conclusions

Our MR analysis findings indicate a potential correlation between SSOE and a protective effect against lung cancer development. Further investigation is imperative to uncover the precise mechanistic link between them.

A novel prognostic signature based on smoking-associated genes for predicting prognosis and immune microenvironment in NSCLC smokers

BACKGROUND

As a highly heterogeneous tumor, non-small cell lung cancer (NSCLC) is famous for its high incidence and mortality worldwide. Smoking can cause genetic changes, which leading to the occurrence and progress of NSCLC. Nevertheless, the function of smoking-related genes in NSCLC needs more research.

METHODS

We downloaded transcriptome data and clinicopathological parameters from Gene Expression Omnibus (GEO) databases, and screened smoking-related genes. Lasso regression were applied to establish the 7-gene signature. The associations between the 7-gene signature and immune microenvironment analysis, survival analysis, drug sensitivity analysis and enriched molecular pathways were studied. Ultimately, cell function experiments were conducted to research the function of FCGBP in NSCLC.

RESULTS

Through 7-gene signature, NSCLC samples were classified into high-risk group (HRG) and low-risk group (LRG). Significant difference in overall survival (OS) between HRG and LRG was found. Nomograms and ROC curves indicated that the 7-gene signature has a stable ability in predicting prognosis. Through the analysis of immune microenvironment, we found that LRG patients had better tumor immune activation. FCGBP showed the highest mutation frequency among the seven prognostic smoking related genes (LRRC31, HPGD, FCGBP, SPINK5, CYP24A1, S100P and FGG), and was notable down-regulated in NSCLC smokers compared with non-smoking NSCLC patients. The cell experiments confirmed that FCGBP knockdown promoting proliferation, migration, and invasion in NSCLC cells.

CONCLUSION

This smoking-related prognostic signature represents a promising tool for assessing prognosis and tumor microenvironment in smokers with NSCLC. The role of FCGBP in NSCLC was found by cell experiments, which can be served as diagnostic biomarker and immunotherapy target for NSCLC.

Airway basal cell‑derived exosomes suppress epithelial‑mesenchymal transition of lung cells by inhibiting the expression of ANO1

Disruption of the epithelial-mesenchymal transition (EMT) of activated lung cells is an important strategy to inhibit the progression of idiopathic pulmonary fibrosis (IPF). The present study investigated the role of exosomes derived from airway basal cells on EMT of lung cells and elucidate the underlying mechanism. Exosomes were characterized by nanoparticle tracking analysis, transmission electron microscopy imaging and markers detection. The role of exosome on the EMT of lung epithelial cells and lung fibroblasts induced by TGF-β1 was detected. RNA sequencing screened dysregulated genes in exosome-treated group, followed by the bioinformatical analysis. One of the candidates, anoctamin-1 (ANO1), was selected for further gain-and-loss phenotype assays. A bleomycin-induced pulmonary fibrosis model was used to evaluate the treatment effect of exosomes. Exosomes were round-like and positively expressed CD63 and tumor susceptibility gene 101 protein. Treatment with exosomes inhibited the EMT of lung cells activated by TGF-β1. 4158 dysregulated genes were identified in exosome-treated group under the threshold of |log2 fold-change| value >1 and they were involved in the metabolism of various molecules, as well as motility-related biological processes. A key gene, ANO1, was verified by reverse transcription-quantitative PCR, whose overexpression induced the EMT of lung cells. By contrast, ANO1 knockdown reversed the EMT induced by TGF-β1. In vivo assay indicated that exosome treatment ameliorated pulmonary fibrosis and inhibited the upregulation of ANO1 induced by bleomycin. In conclusion, airway basal cell-derived exosomes suppressed the EMT of lung cells via the downregulation of ANO1. These exosomes represent a potential therapeutic option for patients with IPF.

MRPS16 promotes lung adenocarcinoma growth via the PI3K/AKT/Frataxin signalling axis

Although MRPS16 is involved in cancer development, its mechanisms in developing LAUD remain unclear. Herein, qRT-PCR, WB and IHC were utilized for evaluating MRPS16 expression levels, while functional assays besides animal experiments were performed to measure MRPS16 effect on LAUD progression. Using WB, the MRPS16 effect on PI3K/AKT/Frataxin signalling pathway was tested. According to our study, MRPS16 was upregulated in LAUD and was correlated to the advanced TNM stage as well as poor clinical outcomes, which represent an independent prognostic factor. Based on functional assays, MRPS16 is involved in promoting LAUD growth, migration and invasion, which was validated further in subsequent analyses through PI3K/AKT/Frataxin pathway activation. Moreover, MRPS16-knockdown-mediated Frataxin overexpression was shown to restore the reduction in tumour cells proliferation, migration and invasion. Our results revealed that MRPS16 caused an aggressive phenotype to LAUD and was a poor prognosticator; thus, targeting MRPS16 may be effectual in LAUD treatment.

Progress in the research of cuproptosis and possible targets for cancer therapy

Developing novel cancer therapies that exploit programmed cell death pathways holds promise for advancing cancer treatment. According to a recently published study in Science, copper death (cuproptosis) occurs when intracellular copper is overloaded, triggering aggregation of lipidated mitochondrial proteins and Fe-S cluster proteins. This intriguing phenomenon is triggered by the instability of copper ions. Understanding the molecular mechanisms behind cuproptosis and its associated genes, as identified by Tsvetkov, including ferredoxin 1, lipoic acid synthase, lipoyltransferase 1, dihydrolipid amide dehydrogenase, dihydrolipoamide transacetylase, pyruvate dehydrogenase α1, pyruvate dehydrogenase β, metallothionein, glutaminase, and cyclin-dependent kinase inhibitor 2A, may open new avenues for cancer therapy. Here, we provide a new understanding of the role of copper death and related genes in cancer.

Exploration of cuprotosis-related genes for predicting prognosis and immunological characteristics in acute myeloid leukaemia based on genome and transcriptome

BACKGROUND

Acute myeloid leukemia (AML) is a common hematologic malignancy with a generally unfavorable prognosis. Cuprotosis as a new form of programmed cell death has been shown to play an important role in tumorigenesis and progression; However, the relationship between cuprotosis and the prognosis of AML patients remains unclear.

METHODS

Transcriptomic and genomics data, along with clinical information, were obtained from the TCGA and GEO databases. Especially, unsupervised clustering and machining learning were used to identify molecular subtypes and cuprotosis-related risk scores respectively. Kaplan-Meier analysis, univariate and multivariate Cox regression, and Receiver Operator Characteristic curve (ROC) were performed to assess the prognosis based on cuprotosis-related genes (CRGs). Moreover, multiple algorithms were used to evaluate immunological heterogeneity among patients with different risk scores. For in vitro analysis, the expression of genes involved in CRGs was detected by Quantitative Reverse Transcription Polymerase (qRT-PCR) in AML patients.

RESULTS

Transcriptomic and genome data indicated the immense heterogeneity in the CRGs landscape of normal and tumor samples. Cuprotosis subtype A and cuprotosis regulatory subtype B in the genomics map and biological characteristics were significantly different from the other groups. Furthermore, these two subtypes had lower risk scores and longer survival times compared to other groups. Cox analyses indicated that risk score was an independent prognostic factor for AML patients. In addition, our risk score could be an indicator of survival outcomes in immunotherapy datasets.

CONCLUSIONS

Our study demonstrates the potential of CRGs in guiding the prognosis, treatment, and immunological characteristics of AML patients.

TGF-β Signaling in Progression of Oral Cancer

Oral cancer is a common malignancy worldwide, accounting for 1.9% to 3.5% of all malignant tumors. Transforming growth factor β (TGF-β), as one of the most important cytokines, is found to play complex and crucial roles in oral cancers. It may act in a pro-tumorigenic and tumor-suppressive manner; activities of the former include cell cycle progression inhibition, tumor microenvironment preparation, apoptosis promotion, stimulation of cancer cell invasion and metastasis, and suppression of immune surveillance. However, the triggering mechanisms of these distinct actions remain unclear. This review summarizes the molecular mechanisms of TGF-β signal transduction, focusing on oral squamous cell and salivary adenoid systemic carcinomas as well as keratocystic odontogenic tumors. Both the supporting and contrary evidence of the roles of TGF-β is discussed. Importantly, the TGF-β pathway has been the target of new drugs developed in the past decade, some having demonstrated promising therapeutic effects in clinical trials. Therefore, the achievements of TGF-β pathway-based therapeutics and their challenges are also assessed. The summarization and discussion of the updated knowledge of TGF-β signaling pathways will provide insight into the design of new strategies for oral cancer treatment, leading to an improvement in oral cancer outcomes.

Germline genetic variants and pediatric rhabdomyosarcoma outcomes: a report from the Children's Oncology Group

BACKGROUND

Relative to other pediatric cancers, survival for rhabdomyosarcoma (RMS) has not improved in recent decades, suggesting the need to enhance risk stratification. Therefore, we conducted a genome-wide association study for event-free survival (EFS) and overall survival (OS) to identify genetic variants associated with outcomes in individuals with RMS.

METHODS

The study included 920 individuals with newly diagnosed RMS who were enrolled in Children's Oncology Group protocols. To assess the association of each single nucleotide polymorphism (SNP) with EFS and OS, we estimated hazard ratios (HRs) and 95% confidence intervals (CIs) using multivariable Cox proportional hazards models, adjusted for clinical covariates. All statistical tests were two sided. We also performed stratified analyses by histological subtype (alveolar and embryonal RMS) and carried out sensitivity analyses of statistically significant SNPs by PAX3/7-FOXO1 fusion status and genetic ancestry group.

RESULTS

We identified that rs17321084 was associated with worse EFS (HR = 2.01, 95% CI = 1.59 to 2.53, P = 5.39 × 10-9) and rs10094840 was associated with worse OS (HR = 1.84, 95% CI = 1.48 to 2.27, P = 2.13 × 10-8). Using publicly available data, we found that rs17321084 lies in a binding region for transcription factors GATA2 and GATA3, and rs10094840 is associated with SPAG1 and RNF19A expression. We also identified that CTNNA3 rs2135732 (HR = 3.75, 95% CI = 2.34 to 5.99, P = 3.54 × 10-8) and MED31 rs74504320 (HR = 3.21, 95% CI = 2.12 to 4.86, P = 3.60 × 10-8) were associated with worse OS among individuals with alveolar RMS.

CONCLUSIONS

We demonstrated that common germline variants are associated with EFS and OS among individuals with RMS. Additional replication and investigation of these SNP effects may further support their consideration in risk stratification protocols.

The identification of a two-gene prognostic model based on cisplatin resistance-related ceRNA network in small cell lung cancer

BACKGROUND

Small cell lung cancer (SCLC) is a very malignant tumor with rapid growth and early metastasis. Platinum-based chemo-resistance is the major issue for SCLC treatment failure. Identifying a new prognostic model will help to make an accurate treatment decision for SCLC patients.

METHODS

Using the genomics of drug sensitivity in cancer (GDSC) database, we identified cisplatin resistance-related lncRNAs in SCLC cells. Based on the competing endogenous RNA (ceRNA) network, we identified the mRNAs correlated with the lncRNAs. Using Cox and LASSO regression analysis, a prognostic model was established. The survival prediction accuracy was evaluated by receiver operating characteristic (ROC) curve and Kaplan-Meier analysis. GSEA, GO, KEGG and CIBERSORT tools were used for functional enrichment and immune cells infiltration analysis.

RESULTS

We first screened out 10 differentially expressed lncRNAs between cisplatin resistant and sensitive SCLC cells from GDSC database. Based on ceRNA network, 31 mRNAs were identified with a correlation with the 10 lncRNAs. Furthermore, two genes (LIMK2 and PI4K2B) were identified by Cox and LASSO regression analysis to construct a prognostic model. Kaplan-Meier analysis indicated that the high-risk group had a poor overall survival compared with the low-risk group. The predicted area under the ROC curve (AUC) was 0.853 in the training set, and the AUC was 0.671 in the validation set. In the meanwhile, the low expression of LIMK2 or the high expression of PI4K2B in SCLC tumors was also significantly associated with poor overall survival in both training and validation sets. Functional enrichment analysis showed that the low-risk group was enriched in the apoptosis pathway and high immune infiltration of T cells. Finally, an apoptosis-related gene Cathepsin D (CTSD) was identified to be up-regulated in the low-risk group, and its higher expression correlated with better overall survival in SCLC.

CONCLUSION

We established a prognostic model and potential biomarkers (LIMK2, PI4K2B and CTSD), which could help to improve the risk stratification of SCLC patients.

Down-regulation of FBP1 in lung adenocarcinoma cells promotes proliferation and invasion through SLUG mediated epithelial mesenchymal transformation

Background

Metabolic reprogramming and epithelial-mesenchymal transformation (EMT) play an important role in lung cancer. In recent studies, metabolic enzymes such as Fructose-1,6-bisphosphatase 1 (FBP1) have shown potential functions beyond regulating metabolism.

Methods

Western blot assay was performed to detect glycolysis-related and EMT-related protein expression levels. The glucose uptake kit and adenosine triphosphate (ATP) detection kit were used to detect glucose uptake rate and ATP content. Transwell assay was used to determine the invasiveness of lung adenocarcinoma cells. Wound healing assay was used to determine the metastatic ability of lung adenocarcinoma cells. Methyl thiazolyl tetrazolium (MTT) assay and EdU staining were performed to investigate the effect of FBP1 overexpression on lung adenocarcinoma proliferation.

Results

Overexpression of FBP1 down-regulated glycolysis-related protein levels and inhibited glucose uptake and ATP production, while knockdown of FBP1 had the opposite effect. Overexpression of FBP1 reversed EMT and inhibited Slug expression. Meanwhile, overexpression of FBP1 impaired the invasion, metastasis and proliferation ability of lung adenocarcinoma cells. In contrast, FBP1 knockdown promoted the EMT process, up-regulated Slug expression and enhanced the invasion, metastasis and proliferation of lung adenocarcinoma cells.

Conclusions

Therefore, FBP1 can be used as one of the potential clinical targets through inhibiting glycolysis, cell invasion and proliferation by inhibiting Slug mediated EMT processes.

LincRNA ZNF529-AS1 inhibits hepatocellular carcinoma via FBXO31 and predicts the prognosis of hepatocellular carcinoma patients

BACKGROUND

Invasion and metastasis of hepatocellular carcinoma (HCC) is still an important reason for poor prognosis. LincRNA ZNF529-AS1 is a recently identified tumour-associated molecule that is differentially expressed in a variety of tumours, but its role in HCC is still unclear. This study investigated the expression and function of ZNF529-AS1 in HCC and explored the prognostic significance of ZNF529-AS1 in HCC.

METHODS

Based on HCC information in TCGA and other databases, the relationship between the expression of ZNF529-AS1 and clinicopathological characteristics of HCC was analysed by the Wilcoxon signed-rank test and logistic regression. The relationship between ZNF529-AS1 and HCC prognosis was evaluated by Kaplan‒Meier and Cox regression analyses. The cellular function and signalling pathways involved in ZNF529-AS1 were analysed by GO and KEGG enrichment analysis. The relationship between ZNF529-AS1 and immunological signatures in the HCC tumour microenvironment was analysed by the ssGSEA algorithm and CIBERSORT algorithm. HCC cell invasion and migration were investigated by the Transwell assay. Gene and protein expression were detected by PCR and western blot analysis, respectively.

RESULTS

ZNF529-AS1 was differentially expressed in various types of tumours and was highly expressed in HCC. The expression of ZNF529-AS1 was closely correlated with the age, sex, T stage, M stage and pathological grade of HCC patients. Univariate and multivariate analyses showed that ZNF529-AS1 was significantly associated with poor prognosis of HCC patients and could be an independent prognostic indicator of HCC. Immunological analysis showed that the expression of ZNF529-AS1 was correlated with the abundance and immune function of various immune cells. Knockdown of ZNF529-AS1 in HCC cells inhibited cell invasion and migration and inhibited the expression of FBXO31.

CONCLUSION

ZNF529-AS1 could be a new prognostic marker for HCC. FBXO31 may be the downstream target of ZNF529-AS1 in HCC.

Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso

In clinical trials, identification of prognostic and predictive biomarkers has became essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to identify patients with potential benefit from the treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied. A new method is required to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data where biomarkers are highly correlated. We propose a novel approach called PPLasso, that integrates prognostic and predictive effects into one statistical model. PPLasso also takes into account the correlations between biomarkers that can alter the biomarker selection accuracy. Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the traditional Lasso and other extensions on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic and proteomic data.

A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome-Interactome Signature for Predicting Non-Small Cell Lung Cancer

The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting NSCLC. The differentially expressed genes (DEGs) were identified in NSCLC compared with normal tissues using TCGA and GTEx data. A biological network was constructed using DEGs, and the top 20 upregulated and 20 downregulated hub genes were identified. These hub genes were used to identify signature genes with penalized logistic regression using the LASSO to predict NSCLC. Our model’s development involved the following steps: (i) the dataset was divided into 80% for training (TR) and 20% for testing (TD1); (ii) a LASSO logistic regression analysis was performed on the TR with 10-fold cross-validation and identified a combination of 17 genes as NSCLC predictors, which were used further for development of the LASSO model. The model’s performance was assessed on the TD1 dataset and achieved an accuracy and an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.986 and 0.998, respectively. Furthermore, the performance of the LASSO model was evaluated using three independent NSCLC test datasets (GSE18842, GSE27262, GSE19804) and achieved high accuracy, with an AUC-ROC of >0.99, >0.99, and 0.95, respectively. Based on this study, a web application called NSCLCpred was developed to predict NSCLC.

Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

BACKGROUND

The mechanism of cuproptosis, a novel copper-induced cell death by regulating tricarboxylic acid cycle (TCA)-related genes, has been reported to regulate oxidative phosphorylation system (OXPHOS) in cancers and can be regarded as potential therapeutic strategies in cancer; however, the characteristics of cuproptosis in pan-cancer have not been elucidated.

METHODS

The multi-omics data of The Cancer Genome Atlas were used to evaluate the cuproptosis-associated characteristics across 32 tumor types. A cuproptosis enrichment score (CEScore) was established using a single sample gene enrichment analysis (ssGSEA) in pan-cancer. Spearman correlation analysis was used to identify pathway most associated with CEScore. Lasso-Cox regression was used to screen prognostic genes associated with OXPHOS and further construct a cuproptosis-related prognostic model in clear cell renal cell carcinoma (ccRCC).

RESULTS

We revealed that most cuproptosis-related genes (CRGs) were differentially expressed between tumors and normal tissues, and somatic copy number alterations contributed to their aberrant expression. We established a CEScore index to indicate cuproptosis status which was associated with prognosis in most cancers. The CEScore was negatively correlated with OXPHOS and significantly featured prognosis in ccRCC. The ccRCC patients with high-risk scores show worse survival outcomes and bad clinical benefits of Everolimus (mTOR inhibitor).

CONCLUSIONS

Our findings indicate the importance of abnormal CRGs expression in cancers. In addition, identified several prognostic CRGs as potential markers for prognostic distinction and drug response in the specific tumor. These results accelerate the understanding of copper-induced death in tumor progression and provide cuproptosis-associated novel therapeutic strategies.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

Background

Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial.

Methods

This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa.

Results

Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients.

Conclusions

In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.

Identification of metabolic genes for the prediction of prognosis and tumor microenvironment infiltration in early-stage non-small cell lung cancer

Early-stage non-small cell lung cancer (NSCLC) patients are at substantial risk of poor prognosis. We attempted to develop a reliable metabolic gene-set-based signature that can predict prognosis accurately for early-stage patients. Least absolute shrinkage and selection operator method Cox regression models were performed to filter the most useful prognostic genes, and a metabolic gene-set-based signature was constructed. Forty-two metabolism-related genes were finally identified, and with specific risk score formula, patients were classified into high-risk and low-risk groups. Overall survival was significantly different between the two groups in discovery (HR: 5.050, 95% CI: 3.368-7.574, P < 0.001), internal validation series (HR: 6.044, 95% CI: 3.918-9.322, P < 0.001), GSE30219 (HR: 2.059, 95% CI: 1.510-2.808, P < 0.001), and GSE68456 (HR: 2.448, 95% CI: 1.723-3.477, P < 0.001). Survival receiver operating characteristic curve at the 5 years suggested that the metabolic signature (area under the curve [AUC] = 0.805) had better prognostic accuracy than any other clinicopathological factors. Further analysis revealed the distinct differences in immune cell infiltration and tumor purity reflected by an immune and stromal score between high- and low-risk patients. In conclusion, the novel metabolic signature developed in our study shows robust prognostic accuracy in predicting prognosis for early-stage NSCLC patients and may function as a reliable marker for guiding more effective immunotherapy strategies.

Ginseng-derived nanoparticles inhibit lung cancer cell epithelial mesenchymal transition by repressing pentose phosphate pathway activity

It is unclear whether ginseng-derived nanoparticles (GDNPs) can prevent tumor cell epithelial-mesenchymal transition (EMT). Here, we describe typical characteristics of GDNPs and possible underlying mechanisms for GDNP antitumor activities. First, GDNPs particle sizes and morphology were determined using nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM), respectively, while cellular uptake of PKH67-labeled GDNPs was also assessed. Next, we evaluated GDNPs antitumor effects by determining whether GDNPs inhibited proliferation and migration of five tumor cell lines derived from different cell types. The results indicated that GDNPs most significantly inhibited proliferation and migration of lung cancer-derived tumor cells (A549, NCI-H1299). Moreover, GDNPs treatment also inhibited cell migration, invasion, clonal formation, and adhesion tube formation ability and reduced expression of EMT-related markers in A549 and NCI-H1299 cells in a dose-dependent manner. Meanwhile, Kaplan-Meier analysis of microarray data revealed that high-level thymidine phosphorylase (TP) production, which is associated with poor lung cancer prognosis, was inhibited by GDNPs treatment, as reflected by decreased secretion of overexpressed TP and downregulation of TP mRNA-level expression. In addition, proteomic analysis results indicated that GDNPs affected pentose phosphate pathway (PPP) activity, with ELISA results confirming that GDNPs significantly reduced levels of PPP metabolic intermediates. Results of this study also demonstrated that GDNPs-induced downregulation of TP expression led to PPP pathway inhibition and repression of lung cancer cell metastasis, warranting further studies of nano-drugs as a new and promising class of anti-cancer drugs.

Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Background

Cancer stem cells (CSCs) are implicated in cancer progression, chemoresistance, and poor prognosis; thus, they may be promising therapeutic targets. In this study, we aimed to investigate the prognostic application of differentially expressed CSC-related genes in lung squamous cell carcinoma (LUSC).

Methods

The mRNA stemness index (mRNAsi)-related differentially expressed genes (DEGs) in tumors were identified and further categorized by LASSO Cox regression analysis and 1,000-fold cross-validation, followed by the construction of a prognostic score model for risk stratification. The fractions of tumor-infiltrating immune cells and immune checkpoint genes were analyzed in different risk groups.

Results

We found 404 mRNAsi-related DEGs in LUSC, 77 of which were significantly associated with overall survival. An eight-gene prognostic signature (PPP1R27, TLX2, ANKLE1, TIGD3, AMH, KCNK3, FLRT3, and PPBP) was identified and used to construct a risk score model. The TCGA set was dichotomized into two risk groups that differed significantly (p = 0.00057) in terms of overall survival time (1, 3, 5-year AUC = 0.830, 0.749, and 0.749, respectively). The model performed well in two independent GEO datasets (p = 0.029, 0.033; 1-year AUC = 0747, 0.783; 3-year AUC = 0.746, 0.737; 5-year AUC = 0.706, 0.723). Low-risk patients had markedly increased numbers of CD8+ T cells and M1 macrophages and downregulated immune checkpoint genes compared to the corresponding values in high-risk patients (p < 0.05).

Conclusion

A stemness-related prognostic model based on eight prognostic genes in LUSC was developed and validated. The results of this study would have prognostic and therapeutic implications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02011-0.

Gene Expression Profiles of the Aging Rat Hippocampus Imply Altered Immunoglobulin Dynamics

Aging is a process that leads to the deterioration in physiological functioning of the brain. Prior research has proposed that hippocampal aging is accompanied by genetic alterations in neural, synaptic, and immune functions. Nevertheless, interactome-based interrogations of gene alterations in hippocampal aging, remain scarce. Our study integrated gene expression profiles of the hippocampus from young and aged rats and functionally classified network-mapped genes based on their interactome. Hippocampal differentially expressed genes (DEGs) between young (5-8 months) and aged (21-26 months) male rats (Rattus norvegicus) were retrieved from five publicly available datasets (GSE14505, GSE20219, GSE14723, GSE14724, and GSE14725; 38 young and 29 aged samples). Encoded hippocampal proteins of age-related DEGs and their interactome were predicted. Clustered network DEGs were identified and the highest-ranked was functionally annotated. A single cluster of 19 age-related hippocampal DEGs was revealed, which was linked with immune response (biological process, P = 1.71E-17), immunoglobulin G binding (molecular function, P = 1.92E-08), and intrinsic component of plasma membrane (cellular component, P = 1.25E-06). Our findings revealed dysregulated hippocampal immunoglobulin dynamics in the aging rat brain. Whether a consequence of neurovascular perturbations and dysregulated blood-brain barrier permeability, the role of hippocampal immunoregulation in the pathobiology of aging warrants further investigation.