Identification of a TGF-β signaling-related gene signature for prediction of immunotherapy and targeted therapy for lung adenocarcinoma

Identification and analysis of amino acid metabolism-related subtypes in lung adenocarcinoma

We aimed to explore the role of amino acid metabolism (AAM) and identify biomarkers for prognosis management and treatment of lung adenocarcinoma. Differentially expressed genes (DEGs) associated with AAM in lung adenocarcinoma were selected from public databases. Samples were clustered into varying subtypes using ConsensusClusterPlus based on gene levels. Survival analysis was conducted using a survival package, and immune analysis was performed using ssGSEA and ESTIMATE. Enrichment analysis was performed using gene set enrichment analysis, and a protein-protein interaction network of DEGs between subgroups was established through STRING. Hub genes were screened and verified using survival analysis, and drug sensitivity prediction was performed. One hundred sixty-three DEGs associated with AAM in lung adenocarcinoma were obtained, and two AAM-associated subtypes were identified. Cluster 1 showed higher survival rates and immune levels compared with cluster 2. The two subtypes were mainly enriched in immune-related signaling pathways, such as B cell receptor, Jak-Stat, and natural killer cell-mediated cytotoxicity. In addition, the mutation landscape between the two groups was significantly different. F2, AHSG, and APOA1 were key hub genes that significantly affected the prognosis differences between the two subtypes. Cluster 2 showed higher sensitivity to drugs, such as mithramycin, depsipeptide, and actinomycin than cluster 1. This study identified two AAM-associated gene subtypes and their biomarkers and predicted the immune status and drug treatment sensitivity of varying subtypes. The results are instructive in the clinical treatment of lung adenocarcinoma.NEW & NOTEWORTHY Two amino acid metabolism-related subtypes were identified based on differentially expressed genes associated with amino acid metabolism. Cluster 1 showed higher survival rates and immune levels compared with cluster 2. Cluster 2 showed higher sensitivity to drugs, such as mithramycin, depsipeptide, and actinomycin compared with cluster 1.

Shared Genomic Features Between Lung Adenocarcinoma and Type 2 Diabetes: A Bioinformatics Study

BACKGROUND

Lung adenocarcinoma (LUAD) is a common histopathological variant of non-small cell lung cancer. Individuals with type 2 diabetes (T2DM) face an elevated risk of developing LUAD. We examined the common genomic characteristics between LUAD and T2DM through bioinformatics analysis.

METHODS

We acquired the GSE40791, GSE25724, GSE10072, and GSE71416 datasets. Differentially expressed genes (DEGs) were identified through R software, particularly its version 4.1.3 and analyzed via gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Subsequently, we analyzed the relationship between immune cell infiltration and DEGs. we constructed a protein-protein interaction network using STRING and visualized it with Cytoscape. Moreover, gene modules were identified utilizing the MCODE plugin, and hub genes were selected through the CytoHubba plugin. Additionally, we evaluated the predictive significance of hub genes using receiver operating characteristic curves and identified the final central hub genes. Finally, we forecasted the regulatory networks of miRNA and transcription factors for the central hub genes.

RESULTS

A total of 748 DEGs were identified. Analysis of immune infiltration showed a notable accumulation of effector-memory CD8 T cells, T follicular helper cells, type 1 T helper cells, activated B cells, natural killer cells, macrophages, and neutrophils in both LUAD and T2DM. Moreover, these DEGs were predominantly enriched in immune-related pathways, including the positive regulation of I-κB kinase/NF-κB signaling, positive regulation of immunoglobulin production, cellular response to interleukin-7, and cellular response to interleukin-4. The TGF-β signaling pathway was significantly important among them. Additionally, seven hub genes were identified, including ATR, RFC4, MCM2, NUP155, NUP107, NUP85, and NUP37. Among them, ATR, RFC4, and MCM2 were identified as pivotal hub genes. Additionally, hsa-mir147a, hsa-mir16-5p, and hsa-mir-1-3p were associated with LUAD and T2DM. SP1 (specific protein 1) and KDM5A (lysine-specific demethylase 5A) regulated MCM2, ATR, and RFC4.

CONCLUSIONS

Our study elucidates the common mechanisms of immune response, TGF-β signaling pathway, and natural killer cells in LUAD and T2DM, and identifies ATR, RFC4, and MCM2 as key potential biomarkers and therapeutic targets for the comorbidity of these two conditions.

Unraveling the genetic mysteries of sarcopenia: A bioinformatics approach

Background As life expectancy increases and the global population ages, the incidence of sarcopenia is also increasing, highlighting the need for better diagnosis and treatment methods.ObjectiveTo study the genetic expression of sarcopenia using bioinformatics methods.MethodsA Weighted Gene Coexpression Network Analysis (WGCNA) was conducted to construct coexpression networks, along with protein-protein interaction networks. Diagnostic biomarker potential was evaluated using receiver operating characteristic curves. An analysis of Single-Sample Gene Set Enrichment Analysis (ssGSEA) was performed in order to determine the amount of immune cell infiltration. We analyzed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) enrichment using the KEGG.ResultsWGCNA identified modules linked to bone metabolism, ssGSEA showed unique gene enrichment patterns, and 268 genes were found to be differentially expressed in sarcopenia. Fourteen co-expression modules related to bone metabolism were identified, with one showing a strong positive correlation. KEGG pathway analysis indicated downregulation of the renin-angiotensin system and Alzheimer's disease pathways. The differentially expressed genes were primarily involved in adipocyte differentiation.ConclusionThis study analyzes genetic changes and immune cell patterns in sarcopenia, providing insights into its causes and potential diagnostic markers for future research on treatments.

TGFβ in Pancreas and Colorectal Cancer: Opportunities to Overcome Therapeutic Resistance

TGFβ is a pleiotropic signaling pathway that plays a pivotal role in regulating a multitude of cellular functions. TGFβ has a dual role in cell regulation where it induces growth inhibition and cell death; however, it can switch to a growth-promoting state under cancerous conditions. TGFβ is upregulated in colorectal cancer and pancreatic cancer, altering the tumor microenvironment and immune system and promoting a mesenchymal state. The upregulation of TGFβ in certain cancers leads to resistance to immunotherapy, and attempts to inhibit TGFβ expression have led to reduced therapeutic resistance when combined with chemotherapy and immunotherapy. Here, we review the current TGFβ inhibitor drugs in clinical trials for pancreatic and colorectal cancer, with the goal of uncovering advances in improving clinical efficacy for TGFβ combinational treatments in patients. Furthermore, we discuss the relevance of alterations in TGFβ signaling and germline variants in the context of personalizing treatment for patients who show lack of response to current therapeutics.

S100P facilitates LUAD progression via PKA/c-Jun-mediated tumor-associated macrophage recruitment and polarization

S100P, a member of the S100 calcium-binding protein family, is closely associated with abnormal proliferation, invasion, and metastasis of various cancers. However, its role in the lung adenocarcinoma (LUAD) tumor microenvironment (TME) remains unclear. In this study, we observed specific expression of S100P on tumor cells in LUAD patients through tissue immunofluorescence analysis. Furthermore, this expression was strongly correlated with the recruitment and polarization of tumor-associated macrophages (TAMs). Bioinformatics analysis revealed that high S100P expression is associated with poorer overall survival in LUAD patients. Subsequently, a subcutaneous mouse model demonstrated that S100P promotes recruitment and polarization of TAMs towards the M2 type. Finally, in vitro studies on LUAD cells revealed that S100P enhances the secretion of chemokines and polarizing factors by activating the PKA/c-Jun pathway, which is implicated in TAM recruitment and polarization towards the M2 phenotype. Moreover, inhibition of c-Jun expression impedes the ability of TAMs to infiltrate and polarize towards the M2 phenotype. In conclusion, our study demonstrates that S100P facilitates LUAD cells growth by recruiting M2 TAMs through PKA/c-Jun signaling, resulting in the production of various cytokines. Considering these findings, S100P holds promise as an important diagnostic marker and potential therapeutic target for LUAD.

TGF-β based risk model to predict the prognosis and immune features in glioblastoma

Background

Transforming growth factor-β (TGF-β) is a multifunctional cytokine with an important role in tissue development and tumorigenesis. TGF-β can inhibit the function of many immune cells, prevent T cells from penetrating into the tumor center, so that the tumor cells escape from immune surveillance and lead to low sensitivity to immunotherapy. However, its potential roles in predicting clinical prognosis and tumor microenvironment (TME) immune features need to be deeply investigated in glioblastoma (GBM).

Methods

The TCGA-GBM dataset was obtained from the Cancer Genome Atlas, and the validation dataset was downloaded from Gene Expression Omnibus. Firstly, differentially expressed TGF-β genes (DEGs) were screened between GBM and normal samples. Then, univariate and multivariate Cox analyses were used to identify prognostic genes and develop the TGF-β risk model. Subsequently, the roles of TGF-β risk score in predicting clinical prognosis and immune characteristics were investigated.

Results

The TGF-β risk score signature with an independent prognostic value was successfully developed. The TGF-β risk score was positively correlated with the infiltration levels of tumor-infiltrating immune cells, and the activities of anticancer immunity steps. In addition, the TGF-β risk score was positively related to the expression of immune checkpoints. Besides, the high score indicated higher sensitivity to immune checkpoint inhibitors.

Conclusions

We first developed and validated a TGF-β risk signature that could predict the clinical prognosis and TME immune features for GBM. In addition, the TGF-β signature could guide a more personalized therapeutic approach for GBM.

Comprehensive landscape of TGFβ-related signature in osteosarcoma for predicting prognosis, immune characteristics, and therapeutic response

Osteosarcoma (OS) is a highly heterogeneous malignant bone tumor, and its tendency to metastasize leads to a poor prognosis. TGFβ is an important regulator in the tumor microenvironment and is closely associated with the progression of various types of cancer. However, the role of TGFβ-related genes in OS is still unclear. In this study, we identified 82 TGFβ DEGs based on RNA-seq data from the TARGET and GETx databases and classified OS patients into two TGFβ subtypes. The KM curve showed that the Cluster 2 patients had a substantially poorer prognosis than the Cluster 1 patients. Subsequently, a novel TGFβ prognostic signatures (MYC and BMP8B) were developed based on the results of univariate, LASSO, and multifactorial Cox analyses. These signatures showed robust and reliable predictive performance for the prognosis of OS in the training and validation cohorts. To predict the three-year and five-year survival rate of OS, a nomogram that integrated clinical features and risk scores was also developed. The GSEA analysis showed that the different subgroups analyzed had distinct functions, particularly, the low-risk group was associated with high immune activity and a high infiltration abundance of CD8 T cells. Moreover, our results indicated that low-risk cases had higher sensitivity to immunotherapy, while high-risk cases were more sensitive to sorafenib and axitinib. scRNA-Seq analysis further revealed that MYC and BMP8B were strongly expressed mainly in tumor stromal cells. Finally, in this study, we confirmed the expression of MYC and BMP8B by performing qPCR, WB, and IHC analyses. To conclude, we developed and validated a TGFβ-related signature to accurately predict the prognosis of OS. Our findings might contribute to personalized treatment and making better clinical decisions for OS patients.

Development of a TGF-β signaling-related genes signature to predict clinical prognosis and immunotherapy responses in clear cell renal cell carcinoma

Background

Transforming growth factor (TGF)-β signaling is strongly related to the development and progression of tumor. We aimed to construct a prognostic gene signature based on TGF-β signaling-related genes for predicting clinical prognosis and immunotherapy responses of patients with clear cell renal cell carcinoma (ccRCC).

Methods

The gene expression profiles and corresponding clinical information of ccRCC were collected from the TCGA and the ArrayExpress (E-MTAB-1980) databases. LASSO, univariate and multivariate Cox regression analyses were conducted to construct a prognostic signature in the TCGA cohort. The E-MTAB-1980 cohort were used for validation. Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (ROC) were conducted to assess effectiveness and reliability of the signature. The differences in gene enrichments, immune cell infiltration, and expression of immune checkpoints in ccRCC patients showing different risks were investigated.

Results

We constructed a seven gene (PML, CDKN2B, COL1A2, CHRDL1, HPGD, CGN and TGFBR3) signature, which divided the ccRCC patients into high risk group and low risk group. The K-M analysis indicated that patients in the high risk group had a significantly shorter overall survival (OS) time than that in the low risk group in the TCGA (p < 0.001) and E-MTAB-1980 (p = 0.012). The AUC of the signature reached 0.77 at 1 year, 0.7 at 3 years, and 0.71 at 5 years in the TCGA, respectively, and reached 0.69 at 1 year, 0.72 at 3 years, and 0.75 at 5 years in the E-MTAB-1980, respectively. Further analyses confirmed the risk score as an independent prognostic factor for ccRCC (p < 0.001). The results of ssGSEA that immune cell infiltration degree and the scores of immune-related functions were significantly increased in the high risk group. The CIBERSORT analysis indicated that the abundance of immune cell were significantly different between two risk groups. Furthermore, The risk score was positively related to the expression of PD-1, CTLA4 and LAG3.These results indicated that patients in the high risk group benefit more from immunotherapy.

Conclusion

We constructed a novel TGF-β signaling-related genes signature that could serve as an promising independent factor for predicting clinical prognosis and immunotherapy responses in ccRCC patients.

Progress and perspectives of perioperative immunotherapy in non-small cell lung cancer

Lung cancer is one of the leading causes of cancer-related death. Lung cancer mortality has decreased over the past decade, which is partly attributed to improved treatments. Curative surgery for patients with early-stage lung cancer is the standard of care, but not all surgical treatments have a good prognosis. Adjuvant and neoadjuvant chemotherapy are used to improve the prognosis of patients with resectable lung cancer. Immunotherapy, an epoch-defining treatment, has improved curative effects, prognosis, and tolerability compared with traditional and ordinary cytotoxic chemotherapy, providing new hope for patients with non-small cell lung cancer (NSCLC). Immunotherapy-related clinical trials have reported encouraging clinical outcomes in their exploration of different types of perioperative immunotherapy, from neoadjuvant immune checkpoint inhibitor (ICI) monotherapy, neoadjuvant immune-combination therapy (chemoimmunotherapy, immunotherapy plus antiangiogenic therapy, immunotherapy plus radiotherapy, or concurrent chemoradiotherapy), adjuvant immunotherapy, and neoadjuvant combined adjuvant immunotherapy. Phase 3 studies such as IMpower 010 and CheckMate 816 reported survival benefits of perioperative immunotherapy for operable patients. This review summarizes up-to-date clinical studies and analyzes the efficiency and feasibility of different neoadjuvant therapies and biomarkers to identify optimal types of perioperative immunotherapy for NSCLC.

Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer

Background: TGF-β signaling pathway plays an essential role in tumor progression and immune responses. However, the link between TGF-β signaling pathway-related genes (TSRGs) and clinical prognosis, tumor microenvironment (TME), and immunotherapy in gastric cancer is unclear. Methods: Transcriptome data and related clinical data of gastric cancer were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and 54 TSRGs were obtained from the Molecular Signatures Database (MSigDB). We systematically analyzed the expression profile characteristics of 54 TSRGs in 804 gastric cancer samples and examined the differences in prognosis, clinicopathological features, and TME among different molecular subtypes. Subsequently, TGF-β-related prognostic models were constructed using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to quantify the degree of risk in each patient. Patients were divided into two high- and low-risk groups based on the median risk score. Finally, sensitivity to immune checkpoint inhibitors (ICIs) and anti-tumor agents was assessed in patients in high- and low-risk groups. Results: We identified two distinct TGF-β subgroups. Compared to TGF-β cluster B, TGF-β cluster A exhibits an immunosuppressive microenvironment with a shorter overall survival (OS). Then, a novel TGF-β-associated prognostic model, including SRPX2, SGCE, DES, MMP7, and KRT17, was constructed, and the risk score was demonstrated as an independent prognostic factor for gastric cancer patients. Further studies showed that gastric cancer patients in the low-risk group, characterized by higher tumor mutation burden (TMB), the proportion of high microsatellite instability (MSI-H), immunophenoscore (IPS), and lower tumor immune dysfunction and exclusion (TIDE) score, had a better prognosis, and linked to higher response rate to immunotherapy. In addition, the risk score and anti-tumor drug sensitivity were strongly correlated. Conclusion: These findings highlight the importance of TSRGs, deepen the understanding of tumor immune microenvironment, and guide individualized immunotherapy for gastric cancer patients.

Cuproptosis-related gene index: A predictor for pancreatic cancer prognosis, immunotherapy efficacy, and chemosensitivity

Aim

The term "Cuproptosis" was coined to describe a novel type of cell death triggered by intracellular copper buildup that is fundamentally distinct from other recognized types such as autophagy, ferroptosis, and pyroptosis in recent days. As the underlying mechanism was newly identified, its potential connection to pancreatic adenocarcinoma (PAAD) is still an open issue.

Methods

A set of machine learning algorithms was used to develop a Cuproptosis-related gene index (CRGI). Its immunological characteristics were studied by exploring its implications on the expression of the immunological checkpoints, prospective immunotherapy responses, etc. Moreover, the sensitivity to chemotherapeutic drugs was predicted. Unsupervised consensus clustering was performed to more precisely identify different CRGI-based molecular subtypes and investigate the immunotherapy and chemotherapy efficacy. The expression of DLAT, LIPT1 and LIAS were also investigated, through real-time quantitative polymerase chain reaction (RT-qPCR), western blot, and immunofluorescence staining (IFS).

Results

A novel CRGI was identified and validated. Additionally, correlation analysis revealed major changes in tumor immunology across the high- and low-CRGI groups. Through an in-depth study of each medication, it was determined that the predictive chemotherapeutic efficacy of 32 regularly used anticancer drugs differed between high- and low-CRGI groups. The results of the molecular subtyping provided more support for such theories. Expressional assays performed at transcriptomic and proteomic levels suggested that the aforementioned Cuproptosis-related genes might serve as reliable diagnostic biomarkers in PAAD.

Significance

This is, to the best of our knowledge, the first study to examine prognostic prediction in PAAD from the standpoint of Cuproptosis. These findings may benefit future immunotherapy and chemotherapeutic therapies.