Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis

Transcriptome sequencing reveals inflammation and macrophage heterogeneity in subacromial bursa from degenerative shoulder disorders

PURPOSE

We aimed to investigate the transcriptomic alterations that occur in the subacromial bursa (SAB) following degenerative or traumatic shoulder diseases.

MATERIALS AND METHODS

RNA sequencing was employed to evaluate the transcriptomic alterations of the SAB in individuals afflicted with degenerative rotator cuff tear (RCT), traumatic RCT and proximal humerus fracture (PHF). To gain insights into the biological significance of differentially expressed genes (DEGs), we conducted an enrichment analysis utilizing Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We further utilized single-cell RNA sequencing datasets of SAB from a recently published study to explore the associated cellular dynamics and alterations.

RESULTS

We detected 1,790 up-regulated and 1,964 down-regulated DEGs between degenerative RCT and PHF, 2,085 up-regulated and 1,919 down-regulated DEGs between degenerative RCT and traumatic RCT, and 20 up-regulated and 12 down-regulated DEGs between traumatic RCT and PHF. Given the similar expression pattern between traumatic RCT and PHF, they were integrated as the traumatic group. In comparison with the traumatic group, 1,983 up-regulated and 2,205 down-regulated DEGs were detected in degenerative SAB. Enrichment analysis of up-regulated DEGs uncovered an elevated inflammatory and immunologic responses in degenerative SAB. Single-cell transcriptomic analysis revealed macrophage represented the immune cell with the most DEGs between the degenerative and traumatic RCT.

CONCLUSION

Our results revealed that the SAB in degenerative RCT exhibited a different transcriptional signature compared to that in traumatic RCT, and enrichment analysis showed immunologic and inflammatory activations. Macrophages may play a fundamental role in this process.

Advances in early detection of non-small cell lung cancer: A comprehensive review

BACKGROUND

Lung cancer has the highest mortality rate among malignancies globally. In addition, due to the growing number of smokers there is considerable concern over its growth. Early detection is an essential step towards reducing complications in this regard and helps to ensure the most effective treatment, reduce health care costs, and increase survival rates.

AIMS

To define the most efficient and cost-effective method of early detection in clinical practice.

MATERIALS AND METHODS

We collected the Information used to write this review by searching papers through PUBMED that were published from 2021 to 2024, mainly systematic reviews, meta-analyses and clinical-trials. We also included other older but notable papers that we found essential and valuable for understanding.

RESULTS

EB-OCT has a varied sensitivity and specificity-an average of 94.3% and 89.9 for each. On the other hand, detecting biomarkers via liquid biopsy carries an average sensitivity of 91.4% for RNA molecules detection, and 97% for combined methylated DNA panels. Moreover, CTCs detection did not prove to have a significant role as a screening method due to the rarity of CTCs in the bloodstream thus the need for more blood samples and for enrichment techniques.

DISCUSSION

Although low-dose CT scan (LDCT) is the current golden standard screening procedure, it is accompanied by a highly false positive rate. In comparison to other radiological screening methods, Endobronchial optical coherence tomography (EB-OCT) has shown a noticeable advantage with a significant degree of accuracy in distinguishing between subtypes of non-small cell lung cancer. Moreover, numerous biomarkers, including RNA molecules, circulating tumor cells, CTCs, and methylated DNA, have been studied in the literature. Many of these biomarkers have a specific high sensitivity and specificity, making them potential candidates for future early detection approaches.

CONCLUSION

LDCT is still the golden standard and the only recommended screening procedure for its high sensitivity and specificity and proven cost-effectiveness. Nevertheless, the notable false positive results acquired during the LDCT examination caused a presumed concern, which drives researchers to investigate better screening procedures and approaches, particularly with the rise of the AI era or by combining two methods in a well-studied screening program like LDCT and liquid biopsy. we suggest conducting more clinical studies on larger populations with a clear demographical target and adopting approaches for combining one of these new methods with LDCT to decrease false-positive cases in early detection.

Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1

Introduction

Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations.

Methods

Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff et al. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort.

Results

Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells.

Discussion

Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.

Harnessing lipid metabolism modulation for improved immunotherapy outcomes in lung adenocarcinoma

BACKGROUND

While anti-programmed cell death protein-1 (PD-1) monotherapy has shown effectiveness in treating lung cancer, its response rate is limited to approximately 20%. Recent research suggests that abnormal lipid metabolism in patients with lung adenocarcinoma may hinder the efficacy of anti-PD-1 monotherapy.

METHODS

Here, we delved into the patterns of lipid metabolism in patients with The Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) and their correlation with the immune microenvironment's cellular infiltration characteristics of the tumor. Furthermore, the lipid metabolism score (LMS) system was constructed, and based on the LMS system, we further performed screening for potential agents targeting lipid metabolism. The mechanism of MK1775 was further validated using RNA sequencing, co-culture technology, and in vivo experiments.

RESULTS

We developed an LSM system and identified a potential sensitizing agent, MK1775, which targets lipid metabolism and enhances the effects of anti-PD-1 treatment. Our results demonstrate that MK1775 inhibits tumor progression by influencing lipid crosstalk between tumor cells and tumor-associated macrophages and CD8+T cells, thereby increasing the effectiveness of anti-PD-1 treatment. Further, we found that MK1775 inhibited the phosphatidylinositol 3-kinase(PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling pathway, which on one hand downregulated FASN-mediated synthesis of fatty acids (FAs) to inhibit fatty acid oxidation of tumor-associated macrophages, and on the other hand, promoted IRF-mediated secretion of CXCL10 and CXCL11 to facilitate the infiltration of CD8+ T cells.

CONCLUSIONS

These findings emphasize the important role of lipid metabolism in shaping the complex tumor microenvironment. By manipulating the intricate intricacies of lipid metabolism within the tumor microenvironment, we can uncover and develop promising strategies to sensitize immunotherapy, potentially revolutionizing cancer treatment approaches.

Increased Immune Infiltration and Improved Prognosis of Head and Neck Squamous Cell Carcinoma Associated with Reduced Ancient Ubiquitous Protein 1 Gene Expression

This study aimed to explore the molecular mechanism underlying the prognostic role of ancient ubiquitous protein 1 (AUP1) in head and neck squamous cell carcinoma (HNSCC) and its relationship with the tumor immune microenvironment. Various web resources were used to analyze the differential expression of AUP1 and its role in the HNSCC pathogenesis. A nomogram aimed at predicting 1-, 3-, and 5-year survival rates was developed based on the patient's clinicopathological characteristics and AUP1 expression pattern. Several algorithms and analytical tools were used to explore the correlation between AUP1 expression and sensitivity to immune checkpoint gene therapy by evaluating infiltrating immune cells in patients with HNSCC. Higher AUP1 mRNA and protein expression levels were observed in most tumors and HNSCC than in the normal tissues. High AUP1 expression was an independent predictive risk factor for the overall survival of patients as it was closely associated with the patients' T, M, clinical, and pathological stages and lymphovascular invasion in HNSCC. In conclusion, AUP1 is involved in the occurrence and progression of HNSCC, may be used as an independent prognostic factor in patients with HNSCC, and could serve as a potential intervention target to improve immunotherapy sensitivity in HNSCC.

Single-cell RNA sequencing reveals aberrant sphingolipid metabolism in non-small cell lung cancer impacts tumor-associated macrophages and stimulates angiogenesis via macrophage inhibitory factor signaling

BACKGROUND

Sphingolipids not only serve as structural components for maintaining cell membrane fluidity but also function as bioactive molecules involved in cell signaling and the regulation of various biological processes. Their pivotal role in cancer cell development, encompassing cancer cell proliferation, migration, angiogenesis, and metastasis, has been a focal point for decades. However, the contribution of sphingolipids to the complexity of tumor microenvironment promoting cancer progression has been rarely investigated.

METHODS

Through the integration of publicly available bulk RNA-seq and single-cell RNA-seq data, we conducted a comprehensive analysis to compare the transcriptomic features between tumors and adjacent normal tissues, thus elucidating the intricacies of the tumor microenvironment (TME).

RESULTS

Disparities in sphingolipid metabolism (SLM)-associated genes were observed between normal and cancerous tissues, with the TME characterized by the enrichment of sphingolipid signaling in macrophages. Cellular interaction analysis revealed robust communication between macrophages and cancer cells exhibiting low SLM, identifying the crucial ligand-receptor pair, macrophage inhibitory factor (MIF)-CD74. Pseudo-time analysis unveiled the involvement of SLM in modulating macrophage polarization towards either M1 or M2 phenotypes. Categorizing macrophages into six subclusters based on gene expression patterns and function, the SPP1+ cluster, RGS1+ cluster, and CXCL10+ cluster were likely implicated in sphingolipid-induced M2 macrophage polarization. Additionally, the CXCL10+, AGER+, and FABP4+ clusters were likely to be involved in angiogenesis through their interaction with endothelial cells.

CONCLUSION

Based on multiple scRNA-seq datasets, we propose that a MIF-targeted strategy could potentially impede the polarization from M1 to M2 and impair tumor angiogenesis in low-SLM non-small cell lung cancer (NSCLC), demonstrating its potent antitumor efficacy.

Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation

Lung cancer stands as the most prevalent form of cancer globally, posing a significant threat to human well-being. Due to the lack of effective and accurate early diagnostic methods, many patients are diagnosed with advanced lung cancer. Although surgical resection is still a potential means of eradicating lung cancer, patients with advanced lung cancer usually miss the best chance for surgical treatment, and even after surgical resection patients may still experience tumor recurrence. Additionally, chemotherapy, the mainstay of treatment for patients with advanced lung cancer, has the potential to be chemo-resistant, resulting in poor clinical outcomes. The emergence of liquid biopsies has garnered considerable attention owing to their noninvasive nature and the ability for continuous sampling. Technological advancements have propelled circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), tumor metabolites, tumor-educated platelets (TEPs), and tumor-associated antigens (TAA) to the forefront as key liquid biopsy biomarkers, demonstrating intriguing and encouraging results for early diagnosis and prognostic evaluation of lung cancer. This review provides an overview of molecular biomarkers and assays utilized in liquid biopsies for lung cancer, encompassing CTCs, ctDNA, non-coding RNA (ncRNA), EVs, tumor metabolites, TAAs and TEPs. Furthermore, we expound on the practical applications of liquid biopsies, including early diagnosis, treatment response monitoring, prognostic evaluation, and recurrence monitoring in the context of lung cancer.

Identification of hub genes significantly linked to tuberous sclerosis related-epilepsy and lipid metabolism via bioinformatics analysis

Background

Tuberous sclerosis complex (TSC) is one of the most common genetic causes of epilepsy. Identifying differentially expressed lipid metabolism related genes (DELMRGs) is crucial for guiding treatment decisions.

Methods

We acquired tuberous sclerosis related epilepsy (TSE) datasets, GSE16969 and GSE62019. Differential expression analysis identified 1,421 differentially expressed genes (DEGs). Intersecting these with lipid metabolism related genes (LMRGs) yielded 103 DELMRGs. DELMRGs underwent enrichment analyses, biomarker selection, disease classification modeling, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA) and AUCell analysis.

Results

In TSE datasets, 103 DELMRGs were identified. Four diagnostic biomarkers (ALOX12B, CBS, CPT1C, and DAGLB) showed high accuracy for epilepsy diagnosis, with an AUC value of 0.9592. Significant differences (p < 0.05) in Plasma cells, T cells regulatory (Tregs), and Macrophages M2 were observed between diagnostic groups. Microglia cells were highly correlated with lipid metabolism functions.

Conclusions

Our research unveiled potential DELMRGs (ALOX12B, CBS, CPT1C and DAGLB) in TSE, which may provide new ideas for studying the psathogenesis of epilepsy.

A novel lipid metabolism-based risk model associated with immunosuppressive mechanisms in diffuse large B-cell lymphoma

BACKGROUND

The molecular diversity exhibited by diffuse large B-cell lymphoma (DLBCL) is a significant obstacle facing current precision therapies. However, scoring using the International Prognostic Index (IPI) is inadequate when fully predicting the development of DLBCL. Reprogramming lipid metabolism is crucial for DLBCL carcinogenesis and expansion, while a predictive approach derived from lipid metabolism-associated genes (LMAGs) has not yet been recognized for DLBCL.

METHODS

Gene expression profiles of DLBCL were generated using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The LASSO Cox regression was used to construct an effective predictive risk-scoring model for DLBCL patients. The Kaplan-Meier survival assessment was employed to compare a given risk score with the IPI score and its impact on the survival of DLBCL patients. Functional enrichment examination was performed utilizing the KEGG pathway. After identifying hub genes via single-sample GSEA (ssGSEA), immunohistochemical staining and immunofluorescence were performed on lymph node samples from control and DLBCL patients to confirm these identified genes.

RESULTS

Sixteen lipid metabolism- and survival-associated genes were identified to construct a prognostic risk-scoring approach. This model demonstrated robust performance over various datasets and emerged as an autonomous risk factor for predicting the development of DLBCL patients. The risk score could significantly distinguish the development of DLBCL patients from the low-risk and elevated-risk IPI classes. Results from the inhibitory immune-related pathways and lower immune scores suggested an immunosuppressive phenotype within the elevated-risk group. Three hub genes, MECR, ARSK, and RAN, were identified to be negatively correlated with activated CD8 T cells and natural killer T cells in the elevated-risk score class. Ultimately, it was determined that these three genes were expressed by lymphoma cells but not by T cells in clinical samples from DLBCL patients.

CONCLUSION

The risk level model derived from 16 lipid metabolism-associated genes represents a prognostic biomarker for DLBCL that is novel, robust, and may have an immunosuppressive role. It can compensate for the limitations of the IPI score in predicting overall survival and has potential clinical application value.

Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, and chemosensitivity in colorectal cancer patients

Background

Coagulation is critically involved in the tumor microenvironment, cancer progression, and prognosis assessment. Nevertheless, the roles of coagulation-related long noncoding RNAs (CRLs) in colorectal cancer (CRC) remain unclear. In this study, an integrated computational framework was constructed to develop a novel coagulation-related lncRNA signature (CRLncSig) to stratify the prognosis of CRC patients, predict response to immunotherapy and chemotherapy in CRC, and explore the potential molecular mechanism.

Methods

CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. We performed unsupervised consensus clustering of CRLs to classify patients into distinct molecular subtypes. We then used stepwise regression to establish the CRLncSig risk model, which stratified patients into high- and low-risk groups. Subsequently, diversified bioinformatics algorithms were used to explore prognosis, biological pathway alteration, immune microenvironment, immunotherapy response, and drug sensitivity across patient subgroups. In addition, weighted gene coexpression network analysis was used to construct an lncRNA-miRNA-mRNA competitive endogenous network. Expression levels of CRLncSig, immune checkpoints, and immunosuppressors were determined using RT-qPCR.

Results

We identified two coagulation subclusters and constructed a risk score model using CRLncSig in CRC, where the patients in cluster 2 and the low-risk group had a better prognosis. The cluster and CRLncSig were confirmed as the independent risk factors, and a CRLncSig-based nomogram exhibited a robust prognostic performance. Notably, the cluster and CRLncSig were identified as the indicators of immune cell infiltration, immunoreactivity phenotype, and immunotherapy efficiency. In addition, we identified a new endogenous network of competing CRLs with microRNA/mRNA, which will provide a foundation for future mechanistic studies of CRLs in the malignant progression of CRC. Moreover, CRLncSig strongly correlated with drug susceptibility.

Conclusion

We developed a reliable CRLncSig to predict the prognosis, immune landscape, immunotherapy response, and drug sensitivity in patients with CRC, which might facilitate optimizing risk stratification, guiding the applications of immunotherapy, and individualized treatments for CRC.

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Objectives

Post-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.

Methods

Expression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.

Results

Functional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P &lt; 0.001) and FERM3 (r = 0.728, P &lt; 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.

Conclusion

A total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.

Effects of Lipid Metabolism-Related Genes PTGIS and HRASLS on Phenotype, Prognosis, and Tumor Immunity in Lung Squamous Cell Carcinoma

Background

Lipid metabolism reprogramming played an important role in cancer occurrence, development, and immune regulation. The aim of this study was to identify and validate lipid metabolism-related genes (LMRGs) associated with the phenotype, prognosis, and immunological characteristics of lung squamous cell carcinoma (LUSC).

Methods

In the TCGA cohort, bioinformatics and survival analysis were used to identify lipid metabolism-related differentially expressed genes (DEGs) associated with the prognosis of LUSC. PTGIS/HRASLS knockdown and overexpression effects on the LUSC phenotype were analyzed in vitro experiments. Based on the expression distribution of PTGIS/HRASLS, LUSC patients were divided into two clusters by consensus clustering. Clinical information, prognosis, immune infiltration, expression of immune checkpoints, and tumor mutation burden (TMB) level were compared between the TCGA and GSE4573 cohorts. The genes related to clustering and tumor immunity were screened by weighted gene coexpression network analysis (WGCNA), and the target module genes were analyzed by functional enrichment analysis, protein-protein interaction (PPI) analysis, and immune correlation analysis.

Results

191 lipid metabolism-related DEGs were identified, of which 5 genes were independent prognostic genes of LUSC. PTGIS/HRASLS were most closely related to LUSC prognosis and immunity. RT-qPCR, western blot (WB) analysis, and immunohistochemistry (IHC) showed that the expression of PTGIS was low in LUSC, while HRASLS was high. Functionally, PTGIS promoted LUSC proliferation, migration, and invasion, while HRASLS inhibited LUSC proliferation, migration, and invasion. The two clusters' expression and distribution of PTGIS/HRASLS had the opposite trend. Cluster 1 was associated with lower pathological staging (pT, pN, and pTNM stages), better prognosis, stronger immune infiltration, higher expression of immune checkpoints, and higher TMB level than cluster 2. WGCNA found that 28 genes including CD4 and IL10RA were related to the expression of PTGIS/HRASLS and tumor immune infiltration. PTGIS/HRASLS in the GSE4573 cohort had the same effect on LUSC prognosis and tumor immunity as the TCGA cohort.

Conclusions

PTGIS and HRASLS can be used as new therapeutic targets for LUSC as well as biomarkers for prognosis and tumor immunity, which has positive significance for guiding the immunotherapy of LUSC.

Machine learning methods revealed the roles of immune-metabolism related genes in immune infiltration, stemness, and prognosis of neuroblastoma

BACKGROUND

Immunometabolism plays an important role in neuroblastoma (NB). However, the mechanism of immune-metabolism related genes (IMRGs) in NB remains unclear. This study aimed to explore the effects of IMRGs on the prognosis, immune infiltration and stemness of patients with NB using machine learning methods.

METHODS

R software (v4.2.1) was used to identify the differentially expressed IMRGs, and machine learning algorithm was used to screen the prognostic genes from IMRGs. Then we constructed a prognostic model and calculated the risk scores. The NB patients were grouped according to the prognosis scores. In addition, the genes most associated with the immune infiltration and stemness of NB were analyzed by weighted gene co-expression network analysis (WGCNA).

RESULTS

There were 89 differentially expressed IMRGs between the MYCN amplification and the MYCN non-amplification group, among which CNR1, GNAI1, GLDC and ABCC4 were selected by machine learning algorithm to construct the prognosis model due to their better prediction effect. Both the K-M survival curve and the 5-year Receiver operating characteristic (ROC) curve indicated that the prognosis model could predict the prognosis of NB patients, and there was significant difference in immune infiltration between the two groups according to the median of risk score.

CONCLUSIONS

We verified the effects of IMRGs on the prognosis, immune infiltration and stemness of NB. These findings could provide help for predicting prognosis and developing immunotherapy in NB.

Identification of an Immune-Related Gene Signature Associated with Prognosis and Tumor Microenvironment in Esophageal Cancer

Background

Esophageal cancer (EC) is a common malignant tumor of the digestive system with high mortality and morbidity. Current evidence suggests that immune cells and molecules regulate the initiation and progression of EC. Accordingly, it is necessary to identify immune-related genes (IRGs) affecting the biological behaviors and microenvironmental characteristics of EC.

Methods

Bioinformatics methods, including differential expression analysis, Cox regression, and immune infiltration prediction, were conducted using R software to analyze the Gene Expression Omnibus (GEO) dataset. The Cancer Genome Atlas (TCGA) cohort was used to validate the prognostic signature. Patients were stratified into high- and low-risk groups for further analyses, including functional enrichment, immune infiltration, checkpoint relevance, clinicopathological characteristics, and therapeutic sensitivity analyses.

Results

A prognostic signature was established based on 21 IRGs (S100A7, S100A7A, LCN1, CR2, STAT4, GAST, ANGPTL5, TRAV39, F2RL2, PGLYRP3, KLRD1, TRIM36, PDGFA, SLPI, PCSK2, APLN, TICAM1, ITPR3, MAPK9, GATA4, and PLAU). Compared with high-risk patients, better overall survival rates and clinicopathological characteristics were found in low-risk patients. The areas under the curve of the two cohorts were 0.885 and 0.718, respectively. Higher proportions of resting CD4⁺ memory T lymphocytes, M2 macrophages, and resting dendritic cells and lower proportions of follicular helper T lymphocytes, plasma cells, and neutrophils were found in the high-risk tumors. Moreover, the high-risk group showed higher expression of CD44 and TNFSF4, lower expression of PDCD1 and CD40, and higher TIDE scores, suggesting they may respond poorly to immunotherapy. High-risk patients responded better to chemotherapeutic agents such as docetaxel, doxorubicin, and gemcitabine. Furthermore, IRGs associated with tumor progression, including PDGFA, ITPR3, SLPI, TICAM1, and GATA4, were identified.

Conclusion

Our immune-related signature yielded reliable value in evaluating the prognosis, microenvironmental characteristics, and therapeutic sensitivity of EC and may help with the precise treatment of this patient population.

Construction and experimental validation of an acetylation-related gene signature to evaluate the recurrence and immunotherapeutic response in early-stage lung adenocarcinoma

BACKGROUND

Acetylation is a reversible epigenetic process, playing an important role in the initiation and progression of malignant tumors. However, the prognosis value of acetylation-related genes in the early-stage lung adenocarcinoma (LUAD) remains obscure.

MATERIALS AND METHODS

The acetylation-related genes were collected and clustered based on transcriptome sequencing of the patients with early-stage LUAD from the Cancer Genome Atlas. The genomic divergence analysis, protein-protein interaction network construction, Lasso regression, and univariate Cox regression were used to identify the significant biomarkers for the recurrence of the early-stage LUAD. The multivariate Cox regression was used to establish the predictive model. Gene Expression Omnibus was systemically retrieved and four independent datasets were used for external validation. 23 early-stage LUAD samples were collected from the local hospital to detect the expression difference of the genes in the model. Transfection assays were performed to verify the regulatory ability of the screened gene to the proliferation of LUAD cell lines. The single-cell RNA sequencing of the early-stage LUAD patients and two lung cancer cohorts receiving immunotherapy were utilized to explore the predictive ability of the established model to immunotherapeutic sensitivity.

RESULTS

The clustering based on acetylation-related genes was significantly associated with the recurrence (P < 0.01) and immune infiltration statuses. Through a series of bioinformatical and machine learning methods, RBBP7 and YEATS2 were ultimately identified. Accordingly, a novel gene signature containing RBBP7 and YEATS2 was developed to evaluate the recurrence-free survival of early-stage LUAD, which was then validated in five independent cohorts (pooled hazard ratio = 1.88, 95% confidence interval = 1.49-2.37) and 23 local clinical samples (P < 0.01). The knock-down of YEATS2 obviously suppressed proliferation of H1975 and HCC-827 cells. Single-cell RNA sequencing analyses indicated that RBBP7 and YEATS2 were both associated with the tumor immune response, and the prognosis signature could predict the immunotherapeutic response in two cohorts receiving immunotherapy (P < 0.05; P < 0.01).

CONCLUSIONS

Totally, an acetylation-related gene signature is constructed, helping to evaluate the recurrence and immunotherapeutic effectiveness of early-stage LUAD patients.

Immune and non-immune cell subtypes identify novel targets for prognostic and therapeutic strategy: A study based on intratumoral heterogenicity analysis of multicenter scRNA-seq datasets in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and the leading cause of cancer incidence and mortality worldwide. Despite the improvement of traditional and immunological therapies, the clinical outcome of LUAD is still far from satisfactory. Patients given the same treatment regimen had different responses and clinical outcomes due to the heterogeneity of LUAD. How to identify the targets based on heterogeneity analysis is crucial for treatment strategies. Recently, the single-cell RNA-sequencing (scRNA-seq) technology has been used to investigate the tumor microenvironment (TME) based on cell-specific changes and shows prominently valuable for biomarker prediction. In this study, we systematically analyzed a meta-dataset from the multiple LUAD scRNA-seq datasets in LUAD, identified 15 main types of cells and 57 cell subgroups, and revealed a series of potential biomarkers in M2b, exhausted CD8⁺T, endothelial cells, fibroblast, and metabolic patterns in TME, which further validated with immunofluorescence in clinical cohorts of LUAD. In the prognosis analysis, M0 macrophage and T cell activation were shown correlated to a better prognosis (p<0.05). Briefly, our study provided insights into the heterogeneity of LUAD and assisted in novel therapeutic strategies for clinical outcome improvement.

GDPD5 Related to Lipid Metabolism Is a Potential Prognostic Biomarker in Neuroblastoma

Neuroblastoma (NB) is an extracranial solid tumor in children with poor prognosis in high-risk patients and its pathogenesis and prognostic markers urgently need to be explored. This study aimed to explore potential biomarkers related to NB from the aspect of lipid metabolism. Fifty-eight lipid metabolism-related differentially expressed genes between high-risk NB and non-high-risk NB in the GSE49710 dataset were analyzed using bioinformatics, including 45 down-regulated genes and 13 up-regulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified steroid hormone biosynthesis as an abnormal metabolic pathway in high-risk NB. Survival analysis established a three-gene prognostic model, including ACHE, GDPD5 and PIK3R1. In the test data, the AUCs of the established prognostic models used to predict patient survival at 1, 3 and 5 years were 0.84, 0.90 and 0.91, respectively. Finally, in the SH-SY5Y cell line, it was verified that overexpression of GDPD5 can inhibit cell proliferation and migration, as well as affect the lipid metabolism of SH-SY5Y, but not the sugar metabolism. hsa-miR-592 was predicted to be a potential target miRNA of GDPD5 by bioinformatics. In conclusion, this study develops a lipid-metabolism-related gene-based prognostic model for NB and demonstrates that GDPD5 inhibits SH-SY5Y proliferation and migration and may be targeted by hsa-miR-592 and inhibit SH-SY5Y fat synthesis.

Prognostic signature of lipid metabolism associated LncRNAs predict prognosis and treatment of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most predominant histological subtype of lung cancer. Abnormal lipid metabolism is closely related to the development of LUAD. LncRNAs are involved in the regulation of various lipid metabolism-related genes in various cancer cells including LUAD. Here, we aimed to identify lipid metabolism-related lncRNAs associated with LUAD prognosis and to propose a new prognostic signature.

METHODS

First, differentially expressed lncRNAs (DE-lncRNAs) from the TCGA-LUAD and the GSE31210 dataset were identified. Then the correlation analysis between DE-lncRNAs and lipid metabolism genes was performed to screen lipid metabolism-related lncRNAs. Cox regression analyses were performed in the training set to establish a prognostic model and the model was validated in the testing set and the validation set. Moreover, The role of this model in the underlying molecular mechanisms, immunotherapy, and chemotherapeutic drug sensitivity analysis was predicted by methods such as Gene Set Enrichment Analysis, immune infiltration, tumor mutational burden (TMB), neoantigen, Tumor Immune Dysfunction and Exclusion, chemosensitivity analysis between the high- and low-risk groups. The diagnostic ability of prognostic lncRNAs has also been validated. Finally, we validated the expression levels of selected prognostic lncRNAs by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

The prognostic model was constructed based on four prognostic lncRNAs (LINC00857, EP300-AS1, TBX5-AS1, SNHG3) related to lipid metabolism. The receiver operating characteristic curve (ROC) and Kaplan Meier (KM) curves of the risk model showed their validity. The results of Gene Set Enrichment Analysis suggested that differentially expressed genes in high- and low-risk groups were mainly enriched in immune response and cell cycle. There statistical differences in TMB and neoantigen between high- and low-risk groups. Drug sensitivity analysis suggested that patients with low risk scores may have better chemotherapy outcomes. The results of qRT-PCR were suggesting that compared with the normal group, the expressions of EP300-AS1 and TBX5-AS1 were down-regulated in the tumor group, while the expressions of LINC00857 and SNHG3 were up-regulated. The four prognostic lncRNAs had good diagnostic capabilities, and the overall diagnostic model of the four prognostic lncRNAs was more effective.

CONCLUSION

A total of 4 prognostic lncRNAs related to lipid metabolism were obtained and an effective risk model was constructed.

Comprehensive analysis of a novel signature incorporating lipid metabolism and immune-related genes for assessing prognosis and immune landscape in lung adenocarcinoma

Background

As the crosstalk between metabolism and antitumor immunity continues to be unraveled, we aim to develop a prognostic gene signature that integrates lipid metabolism and immune features for patients with lung adenocarcinoma (LUAD).

Methods

First, differentially expressed genes (DEGs) related to lipid metabolism in LUAD were detected, and subgroups of LUAD patients were identified via the unsupervised clustering method. Based on lipid metabolism and immune-related DEGs, variables were determined by the univariate Cox and LASSO regression, and a prognostic signature was established. The prognostic value of the signature was evaluated by the Kaplan–Meier method, time-dependent ROC, and univariate and multivariate analyses. Five independent GEO datasets were employed for external validation. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to investigate the underlying mechanisms. The sensitivity to common chemotherapeutic drugs was estimated based on the GDSC database. Finally, we selected PSMC1 involved in the signature, which has not been reported in LUAD, for further experimental validation.

Results

LUAD patients with different lipid metabolism patterns exhibited significant differences in overall survival and immune infiltration levels. The prognostic signature incorporated 10 genes and stratified patients into high- and low-risk groups by median value splitting. The areas under the ROC curves were 0.69 (1-year), 0.72 (3-year), 0.74 (5-year), and 0.74 (10-year). The Kaplan–Meier survival analysis revealed a significantly poorer overall survival in the high-risk group in the TCGA cohort (p < 0.001). In addition, both univariate and multivariate Cox regression analyses indicated that the prognostic model was the individual factor affecting the overall survival of LUAD patients. Through GSEA and GSVA, we found that tumor progression and inflammatory and immune-related pathways were enriched in the high-risk group. Additionally, patients with high-risk scores showed higher sensitivity to chemotherapeutic drugs. The in vitro experiments further confirmed that PSMC1 could promote the proliferation and migration of LUAD cells.

Conclusions

We developed and validated a novel signature incorporating both lipid metabolism and immune-related genes for all-stage LUAD patients. This signature can be applied not only for survival prediction but also for guiding personalized chemotherapy and immunotherapy regimens.

Comprehensive Analysis of a Cancer-Immunity Cycle-Based Signature for Predicting Prognosis and Immunotherapy Response in Patients With Colorectal Cancer

Immune checkpoint blockade (ICB) has been recognized as a promising immunotherapy for colorectal cancer (CRC); however, most patients have little or no clinical benefit. This study aimed to develop a novel cancer-immunity cycle–based signature to stratify prognosis of patients with CRC and predict efficacy of immunotherapy. CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the RNA data from Gene Expression Omnibus (GEO) data sets and real-time quantitative PCR (RT-qPCR) data from paired frozen tissues were used for validation. We built a least absolute shrinkage and selection operator (LASSO)-Cox regression model of the cancer-immunity cycle–related gene signature in CRC. Patients who scored low on the risk scale had a better prognosis than those who scored high. Notably, the signature was an independent prognostic factor in multivariate analyses, and to improve prognostic classification and forecast accuracy for individual patients, a scoring nomogram was created. The comprehensive results revealed that the low-risk patients exhibited a higher degree of immune infiltration, a higher immunoreactivity phenotype, stronger expression of immune checkpoint–associated genes, and a superior response to ICB therapy. Furthermore, the risk model was closely related to the response to multiple chemotherapeutic drugs. Overall, we developed a reliable cancer-immunity cycle–based risk model to predict the prognosis, the molecular and immune status, and the immune benefit from ICB therapy, which may contribute greatly to accurate stratification and precise immunotherapy for patients with CRC.