Molecular Characteristics, Clinical Significance, and Cancer Immune Interactions of Angiogenesis-Associated Genes in Gastric Cancer

Integrated Analysis Construct a Tumor-Associated Macrophage Novel Signature with Promising Implications in Predicting the Prognosis and Immunotherapeutic Response of Gastric Cancer Patients

BACKGROUND

Gastric cancer (GC) remains one of the most prevalent malignant tumors worldwide. At present, tumor-associated macrophages (TAMs) are essential in the progression, metastasis, and drug resistance of tumors. Therefore, TAMs can be a crucial target for tumor treatment.

AIMS

We intended to investigate the TAM characteristics in GC and develop a risk signature based on TAM to predict the prognosis of GC patients.

METHODS

The single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data were acquired from a publicly available database. We utilized the Seurat pipeline to process the scRNA-seq data and determine TAM cell types using marker genes. Univariate Cox regression analysis was utilized to examine TAM-related prognostic genes, and then we employed Lasso-Cox regression analysis, and Multivariate Cox regression analysis established a novel risk profile to forecast the clinical value of the model with a new nomogram combining risk profiles and clinicopathological characteristics.

RESULTS

The current study employed scRNA-seq data to identify five TAM clusters in GC, among which four were significantly associated with GC prognosis. Accordingly, we further developed a TAM-related risk signature utilizing nine genes. After evaluation, our model accurately predicted the prognosis of gastric cancer. Generally, GC patients with low TAMS scores exhibited a more favorable prognosis, greater benefits from immunotherapy, and higher levels of immune cell infiltration.

CONCLUSIONS

The prognosis of GC can be effectively predicted by TAM-based risk signatures, and the signature may provide a new perspective for comprehensively guiding clinical diagnosis, prediction, and immunotherapy for gastric cancer.

LASSO regression and WGCNA-based telomerase-associated lncRNA signaling predicts clear cell renal cell carcinoma prognosis and immunotherapy response

OBJECTIVE

To investigate whether telomerase-associated lncRNA expression affects the prognosis and anti-tumor immunity of patients with renal clear cell carcinoma (ccRCC).

METHODS

A series of analyses were performed to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to assess further the association between immune status, tumor microenvironment, and prognostic risk models.

RESULTS

Eight telomerase-associated lncRNAs associated with prognosis were identified and applied to establish a prognostic risk model. Overall survival was higher in the low-risk group.

CONCLUSION

The established prognostic risk model has a good predictive ability for the prognosis of ccRCC patients and provides a new possible therapeutic target for ccRCC.

Nomogram based on dual-energy CT-derived extracellular volume fraction for the prediction of microsatellite instability status in gastric cancer

Purpose

To develop and validate a nomogram based on extracellular volume (ECV) fraction derived from dual-energy CT (DECT) for preoperatively predicting microsatellite instability (MSI) status in gastric cancer (GC).

Materials and methods

A total of 123 patients with GCs who underwent contrast-enhanced abdominal DECT scans were retrospectively enrolled. Patients were divided into MSI (n=41) and microsatellite stability (MSS, n=82) groups according to postoperative immunohistochemistry staining, then randomly assigned to the training (n=86) and validation cohorts (n=37). We extracted clinicopathological characteristics, CT imaging features, iodine concentrations (ICs), and normalized IC values against the aorta (nICs) in three enhanced phases. The ECV fraction derived from the iodine density map at the equilibrium phase was calculated. Univariate and multivariable logistic regression analyses were used to identify independent risk predictors for MSI status. Then, a nomogram was established, and its performance was evaluated by ROC analysis and Delong test. Its calibration performance and clinical utility were assessed by calibration curve and decision curve analysis, respectively.

Results

The ECV fraction, tumor location, and Borrmann type were independent predictors of MSI status (all P < 0.05) and were used to establish the nomogram. The nomogram yielded higher AUCs of 0.826 (0.729-0.899) and 0.833 (0.675-0.935) in training and validation cohorts than single variables (P<0.05), with good calibration and clinical utility.

Conclusions

The nomogram based on DECT-derived ECV fraction has the potential as a noninvasive biomarker to predict MSI status in GC patients.

Unraveling the prognostic significance of RGS gene family in gastric cancer and the potential implication of RGS4 in regulating tumor-infiltrating fibroblast

Regulator of G-protein signaling (RGS) proteins are regulators of signal transduction mediated by G protein-coupled receptors (GPCRs). Current studies have shown that some molecules in the RGS gene family are related to the occurrence, development and poor prognosis of malignant tumors. However, the RGS gene family has been rarely studied in gastric cancer. In this study, we explored the mutation and expression profile of RGS gene family in gastric cancer, and evaluated the prognostic value of RGS expression. Then we established a prognostic model based on RGS gene family and performed functional analysis. Further studies showed that RGS4, as an independent prognostic predictor, may play an important role in regulating fibroblasts in the immune microenvironment. In conclusion, this study explores the value of RGS gene family in gastric cancer, which is of great significance for predicting the prognosis and guiding the treatment of gastric cancer.

Prognostic analysis of related factors of adverse reactions to immunotherapy in advanced gastric cancer and establishment of a nomogram model

BACKGROUND

Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years. However, the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study. In order to determine the association between adverse reaction factors and prognosis, the aim of this study was to conduct a systematic prognostic analysis. By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy, a nomogram model will be established to predict the survival status of patients more accurately.

AIM

To explore the characteristics and predictors of immune-related adverse reactions (irAEs) in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1 (PD-1) inhibitors and to analyze the correlation between irAEs and patient prognosis.

METHODS

A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected. Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred. Clinical features, manifestations, and prognosis of irAEs in the two groups were collected and analyzed. A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs, and the prediction model of irAEs was established. The receiver operating characteristic (ROC) curve was used to evaluate the ability of different indicators to predict irAEs. A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis. The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.

RESULTS

A total of 132 patients were followed up, of whom 63 (47.7%) developed irAEs. We looked at the two groups' clinical features and found that the two groups were statistically different in age ≥ 65 years, Ki-67 index, white blood cell count, neutrophil count, and regulatory T cell (Treg) count (all P < 0.05). Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence (P = 0.030). The ROC curve indicated that Treg + Ki-67 + age (≥ 65 years) combined could predict irAEs well (area under the curve = 0.753, 95% confidence interval: 0.623-0.848, P = 0.001). Results of the Kaplan-Meier survival curve showed that progression-free survival (PFS) was longer in the irAEs group than in the non-irAEs group (P = 0.001). Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS (P = 0.006).

CONCLUSION

The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy. irAEs can affect the patients' PFS and result in longer PFS. Treg + Ki-67 + age (≥ 65 years old) combined can better predict the occurrence of adverse reactions.

Radiogenomics uncovers an interplay between angiogenesis and clinical outcomes in bladder cancer

BACKGROUND

Precision medicine has become a promising clinical treatment strategy for various cancers, including bladder cancer, where angiogenesis plays a critical role in cancer progression. However, the relationship between angiogenesis, immune cell infiltration, clinical outcomes, chemotherapy, and targeted therapy remains unclear.

METHODS

We conducted a comprehensive evaluation of angiogenesis-related genes (ARGs) to identify their association with immune cell infiltration, transcription patterns, and clinical outcomes in bladder cancer. An ARG score was constructed to identify angiogenic subgroups in each sample and we evaluated their predictive performance for overall survival rate and treatment response. In addition, we optimized existing clinical detection protocols by performing image data processing.

RESULTS

Our study revealed the genomic-level mutant landscape and expression patterns of ARGs in bladder cancer specimens. Using analysis, we identified three molecular subgroups where ARG mutations correlated with patients' pathological features, clinical outcomes, and immune cell infiltration. To facilitate clinical applicability, we constructed a precise nomogram based on the ARG score, which significantly correlated with stem cell index and drug sensitivity. Finally, we proposed the radiogenomics model, which combines the precision of genomics with the convenience of radiomics.

CONCLUSION

Our study sheds light on the prognostic characteristics of ARGs in bladder cancer and provides insights into the tumor environment's characteristics to explore more effective immunotherapy strategies. The findings have significant implications for the development of personalized treatment approaches in bladder cancer and pave the way for future studies in this field.

TRIP6 a potential diagnostic marker for colorectal cancer with glycolysis and immune infiltration association

Thyroid hormone receptor interactor 6 (TRIP6) it is an adaptor protein belonging to the zyxin family of LIM proteins, participating in signaling events through interactions with various molecules. Despite this, TRIP6's role in colorectal cancer (CRC), particularly its correlation with glucose metabolism and immune cell infiltration, remains unclear. Through the TCGA and GEO databases, we obtained RNA sequencing data to facilitate our in-depth study and analysis of TRIP6 expression. To investigate the prognostic value of TRIP6 in CRC, we also used univariate Cox regression analysis. In addition, this study also covered a series of analyses, including clinicopathological analysis, functional enrichment analysis, glycolysis correlation analysis, immunoinfiltration analysis, immune checkpoint analysis, and angiogenesis correlation analysis, to gain a comprehensive and in-depth understanding of this biological phenomenon. It has been found that TRIP6 expression is significantly upregulated in CRC and correlates with the stage of the disease. Its overexpression portends a worse survival time. Functional enrichment analysis reveals that TRIP6 is associated with focal adhesion and glycolysis. Mechanistically, TRIP6 appears to exert its tumorigenic effect by regulating the glycolysis-related gene GPI. A higher level of expression of TRIP6 is associated with an increase in the number of iDC immune cells and a decrease in the number of Th1 immune cells. Also, TRIP6 may promote angiogenesis in tumor cells by promoting the expression of JAG2. Our study uncovers the upregulation of TRIP6 in CRC, illuminating its prognostic and diagnostic value within this context. Furthermore, we examine the relationship between TRIP6 expression levels, glycolysis, angiogenesis and immune cell infiltration. This underscores its potential as a biomarker for CRC treatment and as a therapeutic target.

An angiogenesis-associated gene-based signature predicting prognosis and immunotherapy efficacy of head and neck squamous cell carcinoma patients

OBJECTIVES

To develop a model that can assist in the diagnosis and prediction of prognosis for head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

Data from TCGA and GEO databases were used to generate normalized gene expression data. Consensus Cluster Plus was used for cluster analysis and the relationship between angiogenesis-associated gene (AAG) expression patterns, clinical characteristics and survival was examined. Support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) analyzes and multiple logistic regression analyzes were performed to determine the diagnostic model, and a prognostic nomogram was constructed using univariate and multivariate Cox regression analyses. ESTIMATE, XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, CIBERSORT algorithms were used to assess the immune microenvironment of HNSCC patients. In addition, gene set enrichment analysis, treatment sensitivity analysis, and AAGs mutation studies were performed. Finally, we also performed immunohistochemistry (IHC) staining in the tissue samples.

RESULTS

We classified HNSCC patients into subtypes based on differences in AAG expression from TCGA and GEO databases. There are differences in clinical features, TME, and immune-related gene expression between two subgroups. We constructed a HNSCC diagnostic model based on nine AAGs, which has good sensitivity and specificity. After further screening, we constructed a prognostic risk signature for HNSCC based on six AAGs. The constructed risk score had a good independent prognostic significance, and it was further constructed into a prognostic nomogram together with age and stage. Different prognostic risk groups have differences in immune microenvironment, drug sensitivity, gene enrichment and gene mutation.

CONCLUSION

We have constructed a diagnostic and prognostic model for HNSCC based on AAG, which has good performance. The constructed prognostic risk score is closely related to tumor immune microenvironment and immunotherapy response.

Single-cell and Bulk RNA-Seq reveal angiogenic heterogeneity and microenvironmental features to evaluate prognosis and therapeutic response in lung adenocarcinoma

Background

Angiogenesis stands as a pivotal hallmark in lung adenocarcinoma (LUAD), intricately shaping the tumor microenvironment (TME) and influencing LUAD progression. It emerges as a promising therapeutic target for LUAD, affecting patients' prognosis. However, its role in TME, LUAD prognosis, and its clinical applicability remain shrouded in mystery.

Methods

We employed integrated single-cell and bulk transcriptome sequencing to unravel the heterogeneity of angiogenesis within LUAD cells. Through "consensus clustering", we delineated distinct angiogenic clusters and deciphered their TME features. "Monocle2" was used to unravel divergent trajectories within malignant cell subpopulations of LUAD. Additionally, regulon submodules and specific cellular communication patterns of cells in different angiogenic states were analyzed by "pyscenic" and "Cellchat" algorithms. The "univariate Cox" and "LASSO" algorithms were applied to build angiogenic prognostic models. Immunohistochemistry (IHC) on clinical samples validated the role of model factors in LUAD angiogenesis. We utilized CTRP 2.0 and PRISM databases for pinpointing sensitive drugs against lung adenocarcinoma.

Results

Two clusters for the activation of angiogenesis were identified, with Cluster 1 showing a poor prognosis and a pro-cancerous TME. Three differentiated states of malignant epithelial LUAD cells were identified, which had different degrees of angiogenic activation, were regulated by three different regulon submodules, and had completely different crosstalk from other cells in TME. The experiments validate that SLC2A1 promotes angiogenesis in LUAD. ARS (Angiogenesis related score) had a high prognostic value; low ARSs showed immunotherapy benefits, whereas high ARSs were sensitive to 15 chemotherapeutic agents.

Conclusion

The assessment of angiogenic clusters helps to determine the prognostic and TME characteristics of LUAD. Angiogenic prognostic models can be used to assess the prognosis, immunotherapeutic response, and chemotherapeutic drug sensitivity of LUAD.

A novel anoikis-related gene signature identifies LYPD1 as a novel therapy target for bladder cancer

Bladder cancer (BLCA) is a malignant tumor associated with unfavorable outcomes. Studies suggest that anoikis plays a crucial role in tumor progression and cancer cell metastasis. However, its specific role in bladder cancer remains poorly understood. Our objective was to identify anoikis-related genes (ARGs) and subsequently construct a risk model to assess their potential for predicting the prognosis of bladder cancer.The transcriptome data and clinical data of BLCA patients were sourced from The Cancer Genome Atlas and GEO database. We then performed the differential expression analysis to screen differentially expressed ARGs. Subsequently, we conducted non-negative matrix factorization (NMF) clustering analysis to establish molecular subtypes based on the differentially expressed ARGs. The CIBERSORT algorithm was used to estimate the quantification of different cell infiltration in BLCA tumor microenviroment. A prognostic risk model containing 7 ARGs was established using Lasso-Cox regression analysis. The nomogram was built for predicting the survival probability of BLCA patients. To determine the drug sensitivity of each sample from the high- and low-risk groups, the R package "pRRophetic" was performed. Finally, the role of LYPD1 was explored in BLCA cell lines.We identified 90 differential expression ARGs and NMF clustering categorizated the BLCA patientss into two distinct groups (cluster A and B). Patients in cluster A had a better prognosis than those in cluster B. Then, we established a ARGs risk model including CALR, FASN, FOSL1, JUN, LYPD1, MST1R, and SATB1, which was validated in the train and test set. The results suggested overall survival rate was much higher in low risk group than high risk group. The cox regression analysis, ROC curve analysis, and nomogram collectively demonstrated that the risk model served as an independent prognostic factor. The high risk group had a higher level TME scores compared to the low risk group. Furthermore, LYPD1 was low expression in BLCA cells and overexpression of LYPD1 inhibits the prolifearation, migration and invasion.In the current study, we have identified differential expression ARGs and constructed a risk model with the promise for guiding prognostic predictions and provided a therapeutic target for patients with BLCA.

Targeting tumor and bone microenvironment: Novel therapeutic opportunities for castration-resistant prostate cancer patients with bone metastasis

Despite standard hormonal therapy that targets the androgen receptor (AR) attenuates prostate cancer (PCa) effectively in the initial stage, the tumor ultimately converts to castration-resistant prostate cancer (CRPC), and the acquired resistance is still a great challenge for the management of advanced prostate cancer patients. The tumor microenvironment (TME) consists of multiple cellular and noncellular agents is well known as a vital role during the development and progression of CRPC by establishing communication between TME and tumor cells. Additionally, as primary prostate cancer progresses towards metastasis, and CRPC always experiences bone metastasis, the TME is conducive to the spread of tumors to the distant sits, particularly in bone. In addition, the bone microenvironment (BME) is also closely related to the survival, growth and colonization of metastatic tumor cells. The present review summarized the recent studies which mainly focused on the role of TME or BME in the CRPC patients with bone metastasis, and discussed the underlying mechanisms, as well as the potential therapeutic values of targeting TME and BME in the management of metastatic CRPC patients.

Integrated analyses delineate distinctive immunological pathways and diagnostic signatures for Behcet's disease by leveraging gene microarray data

Behcet's disease (BD) is a chronic inflammatory vasculitis and clinically heterogeneous disorder caused by immunocyte aberrations. Comprehensive research on gene expression patterns in BD illuminating its aetiology is lacking. E-MTAB-2713 downloaded from ArrayExpress was analysed to screen differentially expressed genes (DEGs) using limma. Random forest (RF) and neural network (NN) classification models composed of gene signatures were established using the E-MTAB-2713 training set and subsequently verified using GSE17114. Single sample gene set enrichment analysis was used to assess immunocyte infiltration. After identifying DEGs in E-MTAB-2713, pathogen-triggered, lymphocyte-mediated and angiogenesis- and glycosylation-related inflammatory pathways were discovered to be predominant in BD episodes. Gene signatures from the RF and NN diagnostic models, together with genes enriched in angiogenesis and glycosylation pathways, well discriminated the clinical subtypes of BD manifesting as mucocutaneous, ocular and large vein thrombosis involvement in GSE17114. Moreover, a distinctive immunocyte profile revealed T, NK and dendritic cell activation in BD compared to the findings in healthy controls. Our findings suggested that EPHX1, PKP2, EIF4B and HORMAD1 expression in CD14+ monocytes and CSTF3 and TCEANC2 expression in CD16+ neutrophils could serve as combined gene signatures for BD phenotype differentiation. Pathway genes comprising ATP2B4, MYOF and NRP1 for angiogenesis and GXYLT1, ENG, CD69, GAA, SIGLEC7, SIGLEC9 and SIGLEC16 for glycosylation also might be applicable diagnostic markers for subtype identification.

Immune landscape and prognostic gene signatures in gastric cancer: implications for cachexia and clinical outcomes

Cachexia, a debilitating condition that worsens patient outcomes, often accompanies gastric cancer, a malignancy that is prevalent worldwide. The extensive research explored the interconnected molecular and immune aspects of stomach cancer, with a particular emphasis on cachexia. By employing the GEO database, we identified genes that were expressed differently in gastric cancer patients suffering from cachexia. Following the analysis of Weighted Gene Co-expression Network (WGCNA), gene modules intricately linked to particular immune cells were revealed, indicating a significantly disrupted tumor microenvironment. A strong predictive model was developed, centered around key genes such as CAMK4, SLC37A2, and BCL11B. Surprisingly, this particular model not only showed better predictive abilities in comparison to conventional clinical factors but also exhibited a strong connection with increased infiltration of macrophages and T cells. These discoveries suggest the presence of an immune-suppressing and tumor-promoting atmosphere among individuals at a greater risk. Moreover, the utilization of Gene Set Enrichment Analysis (GSEA) established a connection between the genes linked to our risk score and vital immune-related pathways, thereby strengthening the pivotal involvement of immunity in the development of gastric cancer. To summarize, our discoveries provide a more profound comprehension of the molecular and immune mechanisms that support cachexia in gastric cancer, presenting a hopeful basis for upcoming advancements in treatment.

Hierarchical clustering identifies oxidative stress-related subgroups for the prediction of prognosis and immune microenvironment in gastric cancer

Background

Gastric cancer (GC) is a prevalent malignancy of the digestive tract globally, demonstrating a substantial occurrence of relapse and metastasis, alongside the absence of efficacious treatment. Tumor progression and the development of cancer are linked to oxidative stress. Our objective was twofold: first, to determine distinct subcategories based on oxidative stress in GC patients, and second, to establish oxidative stress-related genes that would aid in stratifying the risk for GC patients.

Methods

TCGA-STAD and GSE84437 datasets were utilized to obtain the mRNA expression profiles and corresponding clinical information of GC patients. Through consensus clustering analysis, distinct subgroups related to oxidative stress were identified. To uncover the underlying mechanisms, GSEA and GSVA were performed. xCell, CIBERSORT, MCPCounter, and TIMER algorithms were employed to evaluate the immune microenvironment and immune status of the different GC subtypes. A prognostic risk model was developed using the TCGA-STAD dataset and substantiated using the GSE84437 dataset. Furthermore, qRT-PCR was employed to validate the expression of genes associated with prognosis.

Results

Two distinct subtypes of oxidative stress were discovered, with markedly different survival rates. The C1 subtype demonstrated an activated immune signal pathway, a significant presence of immune cell infiltration, high immune score, and a high microenvironment score, indicating a poor prognosis. Moreover, a prognostic signature related to oxidative stress (IMPACT and PXDN) was able to accurately estimate the likelihood of survival for patients with gastric cancer. A nomogram incorporating the patients' gender, age, and risk score was able to predict survival in gastric cancer patients. Additionally, the expression of IMPACT and PXDN showed a strong correlation with overall survival and the infiltration of immune cells.

Conclusion

Based on signatures related to oxidative stress, we developed an innovative system for categorizing patients with GC. This stratification enables accurate prognostication of individuals with GC.

METTL3 promotes drug resistance to oxaliplatin in gastric cancer cells through DNA repair pathway

Gastric cancer (GC) poses a significant threat to human health and remains a prevalent form of cancer. Despite clinical treatments, the prognosis for Gastric cancer patients is still unsatisfactory, largely due to the development of multidrug resistance. Oxaliplatin (OXA), a second-generation platinum drug, is commonly recommended for adjuvant and palliative chemotherapy in Gastric cancer; however, the underlying mechanisms of acquired resistance to Oxaliplatin in Gastric cancer patients are not yet fully understood. In this study, we aimed to explore the potential mechanisms of Oxaliplatin resistance in Gastric cancer by employing bioinformatics analysis and conducting in vitro experiments. Specifically, we focused on investigating the role of methyltransferase-like 3 (METTL3). Our findings revealed that the knockdown of METTL3 significantly impeded the proliferation and migration of Gastric cancer cells. METTL3 knockdown induced apoptosis in OXA-resistant Gastric cancer cells and enhanced their sensitivity to Oxaliplatin. Furthermore, we found that DNA repair pathways were significantly activated in OXA-resistant Gastric cancer cells, and METTL3 knockdown significantly inhibited DNA repair pathways. Another important finding is that METTL3 knockdown and OXA-induced Gastric cancer cell death are additive, and the targeted METTL3 can assist Oxaliplatin treatment. Collectively, our findings suggest that METTL3 knockdown can augment the sensitivity of Gastric cancer cells to Oxaliplatin by impeding DNA repair processes. Consequently, targeting METTL3 holds great promise as a viable adjuvant strategy in the treatment of Gastric cancer patients.

Prognosis and immunoinfiltration analysis of angiogene-related genes in grade 4 diffuse gliomas

Although angiogenesis critically influences the progression of solid tumors, its contribution to highly malignant, grade 4 diffuse gliomas remains unclear. After analyzing 506 angiogenesis-related genes differentially expressed in grade 4 diffuse gliomas via LASSO and univariate and multivariate COX regression analyses, we constructed a nomogram based on COL22A1, IGFBP2, and MPO that accurately predicted patient survival. The nomogram's performance was validated in an external patient cohort, and a risk score based on the formula COL22A1*0.148+IGFBP2*0.234+MPO*0.145 was used to distinguish high-risk from low-risk patients. Based on differentially expressed genes among risk groups, functional enrichment and drug sensitivity analyses were conducted, and the association between COL22A1, IGFBP2, and MPO expression and infiltrating immune cells and immune checkpoint genes was investigated. We next focused on COL22A1, and verified its overexpression in both glioma cell lines and clinical samples. A pro-oncogenic role for COL22A1, evidenced by impaired proliferation, migration, and invasion capacities, was evidenced upon shRNA-mediated COL22A1 silencing in glioma U87 and LN18 cells. In summary, we present a novel nomogram based on the angiogenesis-related genes COL22A1, IGFBP2, and MPO that allows survival prediction in patients with grade 4 diffuse gliomas. Furthermore, our cellular assays support a pro-oncogenic role for COL22A1 in these tumors.

Comprehensive analysis of ICD-related lncRNAs in predicting risk stratification, clinical prognosis and immune response for breast cancer

BACKGROUND

Breast cancer (BRCA) represents a significant threat with high mortality rates due to relapse, metastasis, and chemotherapy resistance. As a regulated cell death process characterized by the induction of immunogenic signals, immunogenic cell death (ICD) has been identified as an effective anti-tumorigenesis approach. However, the comprehensive study and its clinical value of ICD-related lncRNAs in BRCA is still missing.

METHODS

The transcriptome matrix and corresponding clinical information of BRCA patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was performed to identify ICD-related lncRNAs (ICDRLs). To determine the prognostic value of the identified ICDRLs, univariate Cox regression analysis, LASSO algorithm, and multivariate Cox regression analysis were employed to construct a risk model. The prognostic risk model was subsequently evaluated using univariate and multivariate Cox regression analysis, as well as Nomogram analysis. In vitro experiments were also conducted to validate the bioinformatics findings using quantitative real-time PCR (qRT-PCR).

RESULTS

We established a prognostic risk signature consisting of five ICDRLs. The prognostic value of this model was subsequently confirmed in guiding BRCA prognostic stratification. Furthermore, we explored the correlation of the risk score with various clinical characteristics and chemotherapy response. qRT-PCR result confirmed the abnormal expression of ICDRLs, which was consistent with the bioinformatics data.

CONCLUSIONS

Our findings provide evidence of the critical role of ICDRLs in BRCA and offer a novel perspective for exploring precise treatment options for BRCA patients.

Exploring the crosstalk between endothelial cells, immune cells, and immune checkpoints in the tumor microenvironment: new insights and therapeutic implications

The tumor microenvironment (TME) is a highly intricate milieu, comprising a multitude of components, including immune cells and stromal cells, that exert a profound influence on tumor initiation and progression. Within the TME, angiogenesis is predominantly orchestrated by endothelial cells (ECs), which foster the proliferation and metastasis of malignant cells. The interplay between tumor and immune cells with ECs is complex and can either bolster or hinder the immune system. Thus, a comprehensive understanding of the intricate crosstalk between ECs and immune cells is essential to advance the development of immunotherapeutic interventions. Despite recent progress, the underlying molecular mechanisms that govern the interplay between ECs and immune cells remain elusive. Nevertheless, the immunomodulatory function of ECs has emerged as a pivotal determinant of the immune response. In light of this, the study of the relationship between ECs and immune checkpoints has garnered considerable attention in the field of immunotherapy. By targeting specific molecular pathways and signaling molecules associated with ECs in the TME, novel immunotherapeutic strategies may be devised to enhance the efficacy of current treatments. In this vein, we sought to elucidate the relationship between ECs, immune cells, and immune checkpoints in the TME, with the ultimate goal of identifying novel therapeutic targets and charting new avenues for immunotherapy.

m6A genotypes and prognostic signature for assessing the prognosis of patients with acute myeloid leukemia

BACKGROUND

N6-methyladenosine (m6A) has been confirmed to function critically in acute myeloid leukemia (AML) progression. Hitherto, the subtyping and prognostic predictive significance of m6A-correlated genes in AML is unclear.

METHOD

From The Cancer Genome Atlas (TCGA-LAML), Therapeutically Applicable Research to Generate Effective Treatments (TARGET-AML) and Gene Expression Omnibus (GEO, GSE71014) databases, we collected the sequencing data of AML patients. The batch effect was removed via limma package for TCGA-LAML and TARGET-AML, and the aggregated samples were AML cohorts. Samples in the AML cohort identified m6A models in AML by consensus clustering based on 23-m6A-related modulators. M6A-related differentially expressed genes (m6ARDEGs) influencing the overall survival (OS) of AML were determined by performing differential expression analysis and univariate COX analysis, and consensus-based clustering was utilized to access AML molecular subtypes. LASSO and multivariate COX analyses were performed to obtain the optimized m6ARDEGs to construct the m6A Prognostic Risk Score (m6APR_Score). Whether the model was robust was evaluated according to Kaplan-Meier (K-M) and receiver operator characteristic (ROC) curves. Further, the abundance of immune cell infiltration was explored in different m6A modification patterns and molecular subtypes and m6APR_Score groupings. Finally, nomogram was constructed to predict OS in AML. Quantitative real-time polymerase chain reaction (RT-qPCR) and cell counting kit-8 (CCK-8) assay were used to validate the genes in m6APR_Score in AML cells.

RESULTS

The m6A models (m6AM1, m6AM2, m6AM3) and molecular subtypes (C1, C2, C3) were identified in the AML cohort, exhibiting different prognosis and immunoreactivity. We recognized novel prognostic biomarkers of AML such as CD83, NRIP1, ACSL1, METTL7B, OGT, and C4orf48. AML patients were grouped into high-m6APR_Score and low-m6APR_Score groups, with the later group showing a better prognosis than former one. Both the AML cohort and the validation cohort GSE71014 demonstrated excellent prediction. Finally, the nomogram accurately predicted the survival of patients suffering from AML. Further, the decision curves showed that both nomogram and m6APR_Score showed excellent prediction. It was confirmed in vitro experiments that mRNA expressions of NRIP1, ACSL1, METTL7B and OGT were elevated, while CD83 and C4orf48 mRNA expressions downregulated in AML cells. A significant increase in the viability of U937 and THP-1 cell lines after inhibition of CD83, while siMETTL7B had contrast results.

CONCLUSION

Our study demonstrated that m6APR_Score and CD83, NRIP1, ACSL1, METTL7B, OGT, and C4orf48 potentially provided novel and promising prognostic support for AML patients.

Pan-cancer analysis of G6PD carcinogenesis in human tumors

Glucose-6-phosphate dehydrogenase (G6PD) is involved in the catalytic pentose phosphate pathway (PPP), which is closely related to energy metabolism. G6PD plays a crucial role in many types of cancer, but the specific molecular mechanisms of G6PD in cancer remain unclear. Therefore, we investigated the potential oncogenic role of G6PD in various tumors based on The Cancer Genome Atlas (TCGA), the cBioPortal datasets, the University of California Santa Cruz (UCSC) Xena browser, and the UALCAN-based online tool. G6PD was highly expressed in several cancer tissues (hepatocellular carcinoma, glioma, and breast cancer) compared with normal tissues and was significantly associated with poor prognosis of hepatocellular carcinoma, clear cell renal cell carcinoma, and breast cancer. Promoter methylation levels of G6PD were lower in Bladder Urothelial Carcinoma (BLCA) (P = 2.77e-02), breast invasive carcinoma (BRCA) (P = 1.62e-12), kidney renal clear cell carcinoma (KIRC) (P = 4.23e-02), kidney renal papillary cell carcinoma (KIRP) (P = 2.64e-03), liver hepatocellular carcinoma (LIHC) (P = 1.76e-02), stomach adenocarcinoma (STAD) (P = 3.50e-02), testicular germ cell tumors (TGCT) (P = 1.62e-12), higher in prostate adenocarcinoma (PRAD) (P = 1.81e-09), and uterine corpus endometrial carcinoma (UCEC) (P = 2.96e-04) compared with corresponding normal tissue samples. G6PD expression was positively correlated with the infiltration level of immune cells in most tumors, suggesting that G6PD may be involved in tumor immune infiltration. In addition, the functional mechanism of G6PD also involves 'Carbon metabolism', 'Glycolysis/Gluconeogenesis', 'Pentose phosphate pathway', and 'Central carbon pathway metabolism in cancer signaling pathway'. This pan-cancer study provides a relatively broad understanding of the oncogenic role of G6PD in various tumors and presents a theoretical basis for the development of G6PD inhibitors as therapeutic drugs for multiple cancers.

Construction and Validation of a Reliable Disulfidptosis-Related LncRNAs Signature of the Subtype, Prognostic, and Immune Landscape in Colon Cancer

Disulfidptosis, a novel form of regulated cell death (RCD) associated with metabolism, represents a promising intervention target in cancer therapy. While abnormal lncRNA expression is associated with colon cancer development, the prognostic potential and biological characteristics of disulfidptosis-related lncRNAs (DRLs) remain unclear. Consequently, the research aimed to discover a novel indication of DRLs with significant prognostic implications, and to investigate their possible molecular role in the advancement of colon cancer. Here, we acquired RNA-seq data, pertinent clinical data, and genomic mutations of colon adenocarcinoma (COAD) from the TCGA database, and then DRLs were determined through Pearson correlation analysis. A total of 434 COAD patients were divided in to three subgroups through clustering analysis based on DRLs. By utilizing univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate Cox regression analysis, we ultimately created a prognostic model consisting of four DRLs (AC007728.3, AP003555.1, ATP2B1.AS1, and NSMCE1.DT), and an external database was used to validate the prognostic features of the risk model. According to the Kaplan-Meier curve analysis, patients in the low-risk group exhibited a considerably superior survival time in comparison to those in the high-risk group. Enrichment analysis revealed a significant association between metabolic processes and the genes that were differentially expressed in the high- and low-risk groups. Additionally, significant differences in the tumor immune microenvironment landscape were observed, specifically pertaining to immune cells, function, and checkpoints. High-risk patients exhibited a low likelihood of immune evasion, as indicated by the Tumor Immune Dysfunction and Exclusion (TIDE) analysis. Patients who exhibit both a high risk and high Tumor Mutational Burden (TMB) experience the least amount of time for survival, whereas those belonging to the low-risk and low-TMB category demonstrate the most favorable prognosis. In addition, the risk groups determined by the 4-DRLs signature displayed distinct drug sensitivities. Finally, we confirmed the levels of expression for four DRLs through rt-qPCR in both tissue samples from colon cancer patients and cell lines. Taken together, the first 4-DRLs-based signature we proposed may serve for a hopeful instrument for forecasting the prognosis, immune landscape, and therapeutic responses in colon cancer patients, thereby facilitating optimal clinical decision-making.

Machine learning-based identification of glycosyltransferase-related mRNAs for improving outcomes and the anti-tumor therapeutic response of gliomas

Background: Glycosyltransferase participates in glycosylation modification, and glycosyltransferase alterations are involved in carcinogenesis, progression, and immune evasion, leading to poor outcomes. However, in-depth studies on the influence of glycosyltransferase on clinical outcomes and treatments are lacking. Methods: The analysis of differentially expressed genes was performed using the Gene Expression Profiling Interactive Analysis 2 database. A total of 10 machine learning algorithms were introduced, namely, random survival forest, elastic network, least absolute shrinkage and selection operator, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox, supervised principal components, generalized boosted regression modeling, and survival support vector machine. Gene Set Enrichment Analysis was performed to explore signaling pathways regulated by the signature. Cell-type identification by estimating relative subsets of RNA transcripts was used for estimating the fractions of immune cell types. Results: Here, we analyzed the genomic and expressive alterations in glycosyltransferase-related genes in gliomas. A combination of 80 machine learning algorithms was introduced to establish the glycosyltransferase-related mRNA signature (GRMS) based on 2,030 glioma samples from The Cancer Genome Atlas Program, Chinese Glioma Genome Atlas, Rembrandt, Gravendeel, and Kamoun cohorts. The GRMS was identified as an independent hazardous factor for overall survival and exhibited stable and robust performance. Notably, gliomas in the high-GRMS subgroup exhibited abundant tumor-infiltrating lymphocytes and tumor mutation burden values, increased expressive levels of hepatitis A virus cellular receptor 2 and CD274, and improved progression-free survival when subjected to anti-tumor immunotherapy. Conclusion: The GRMS may act as a powerful and promising biomarker for improving the clinical prognosis of glioma patients.

The role of molecular subtypes and immune infiltration characteristics based on disulfidptosis-associated genes in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer which accounts for about 40% of all lung cancers. Early detection, risk stratification and treatment are important for improving outcomes for LUAD. Recent studies have found that abnormal accumulation of cystine and other disulfide occurs in the cell under glucose starvation, which induces disulfide stress and increases the content of disulfide bond in actin cytoskeleton, resulting in cell death, which is defined as disulfidptosis. Because the study of disulfidptosis is in its infancy, its role in disease progression is still unclear. In this study, we detected the expression and mutation of disulfidptosis genes in LUAD using a public database. Clustering analysis based on disulfidptosis gene was performed and differential genes of disulfidptosis subtype were analyzed. 7 differential genes of disulfidptosis subtype were used to construct a prognostic risk model, and the causes of prognostic differences were investigated by immune-infiltration analysis, immune checkpoint analysis, and drug sensitivity analysis. qPCR was used to verify the expression of 7 key genes in lung cancer cell line (A549) and normal bronchial epithelial cell line (BEAS-2B). Since G6PD had the highest risk factor of lung cancer, we further verified the protein expression of G6PD in lung cancer cells by western blot, and confirmed through colony formation experiment that interference with G6PD was able to significantly inhibit the proliferation ability of lung cancer cells. Our results provide evidence for the role of disulfidptosis in LUAD and provide new ideas for individualized precision therapy of LUAD.

Integrative profiling analysis reveals prognostic significance, molecular characteristics, and tumor immunity of angiogenesis-related genes in soft tissue sarcoma

Background

Soft tissue sarcoma (STS) is a class of malignant tumors originating from mesenchymal stroma with a poor prognosis. Accumulating evidence has proved that angiogenesis is an essential hallmark of tumors. Nevertheless, there is a paucity of comprehensive research exploring the association of angiogenesis-related genes (ARGs) with STS.

Methods

The ARGs were extracted from previous literature, and the differentially expressed ARGs were screened for subsequent analysis. Next, the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were conducted to establish the angiogenesis-related signature (ARSig). The predictive performance of the novel ARSig was confirmed using internal and external validation, subgroup survival, and independent analysis. Additionally, the association of the ARSig with the tumor immune microenvironment, tumor mutational burden (TMB), and therapeutic response in STS were further investigated. Notably, we finally conducted in vitro experiments to verify the findings from the bioinformatics analysis.

Results

A novel ARSig is successfully constructed and validated. The STS with a lower ARSig risk score in the training cohort has an improved prognosis. Also, consistent results were observed in the internal and external cohorts. The receiver operating characteristic (ROC) curve, subgroup survival, and independent analysis further indicate that the novel ARSig is a promising independent prognostic predictor for STS. Furthermore, it is proved that the novel ARSig is relevant to the immune landscape, TMB, immunotherapy, and chemotherapy sensitivity in STS. Encouragingly, we also validate that the signature ARGs are significantly dysregulated in STS, and ARDB2 and SRPK1 are closely connected with the malignant progress of STS cells.

Conclusion

In sum, we construct a novel ARSig for STS, which could act as a promising prognostic factor for STS and give a strategy for future clinical decisions, immune landscape, and personalized treatment of STS.

Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer

Background

Gastric cancer (GC) is the fifth most common cancer and the second leading cause of cancer-related deaths worldwide. Due to the lack of specific markers, the early diagnosis of gastric cancer is very low, and most patients with gastric cancer are diagnosed at advanced stages. The aim of this study was to identify key biomarkers of GC and to elucidate GC-associated immune cell infiltration and related pathways.

Methods

Gene microarray data associated with GC were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were analyzed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia, Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction (PPI) networks. Weighted gene coexpression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to identify pivotal genes for GC and to assess the diagnostic accuracy of GC hub markers using the subjects' working characteristic curves. In addition, the infiltration levels of 28 immune cells in GC and their interrelationship with hub markers were analyzed using ssGSEA. And further validated by RT-qPCR.

Results

A total of 133 DEGs were identified. The biological functions and signaling pathways closely associated with GC were inflammatory and immune processes. Nine expression modules were obtained by WGCNA, with the pink module having the highest correlation with GC; 13 crossover genes were obtained by combining DEGs. Subsequently, the LASSO algorithm and validation set verification analysis were used to finally identify three hub genes as potential biomarkers of GC. In the immune cell infiltration analysis, infiltration of activated CD4 T cell, macrophages, regulatory T cells and plasmacytoid dendritic cells was more significant in GC. The validation part demonstrated that three hub genes were expressed at lower levels in the gastric cancer cells.

Conclusion

The use of WGCNA combined with the LASSO algorithm to identify hub biomarkers closely related to GC can help to elucidate the molecular mechanism of GC development and is important for finding new immunotherapeutic targets and disease prevention.

Characteristic of molecular subtypes based on PANoptosis-related genes and experimental verification of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a type of liver cancer that originates from liver cells. It is one of the most common types of liver cancer and a leading cause of cancer-related death worldwide. Early detection and treatment can improve the HCC prognosis. Therefore, it is necessary to further improve HCC markers and risk stratification. PANoptosome is a cytoplasmic polymer protein complex that regulates a proinflammatory programmed cell death pathway called "PANoptosis". The role of PANoptosis in HCC remains unclear. In this study, the molecular changes of PANoptosis related genes (PAN-RGs) in HCC were systematically evaluated. We characterized the heterogeneity of HCC by using consensus clustering to identify two distinct subtypes. The two subtypes showed different survival rate, biological function, chemotherapy drug sensitivity and immune microenvironment. After identification of PAN-RG differential expression genes (DEGs), a prognostic model was established by Cox regression analysis using minimum absolute contraction and selection operator (LASSO), and its prognostic value was verified by Cox regression analysis, Kaplan-Meier curve and receiver operating characteristic (ROC) curve. Our own specimens were also used to further validate the prognostic significance and possible clinical value of the selected targets. Subsequently, we conducted a preliminary discussion on the reasons for the influence of the model on the prognosis through TME analysis, drug resistance analysis, TMB analysis and other studies. This study provides a new idea for individualized and precise treatment of HCC.

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer

Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

A comprehensive analysis of immunogenic cell death and its key gene HSP90AA1 in bladder cancer

BACKGROUND

Bladder cancer (BLCA) is defined as a type of urinary cancer with high incidence and lack of specific biomarkers and drug targets. Immunogenic cell death (ICD) has been classified as a regulated type of cell death. Growing evidence suggested that ICD can reshape the tumor immune microenvironment, which may contribute to the development of immunotherapy strategies. The aim of this study was to reveal the specific mechanism of ICD in bladder cancer and to further predict the prognostic immunotherapy outcomes.

METHODS

By consensus clustering analysis, bladder cancer patients in TCGA database were divided into different ICD subtypes. Additionally, we developed an ICD-scoring system and constructed the ICD score-based risk signature and nomogram to better characterize patients. Furthermore, we carried out a series of experiments to verify the relevant findings.

RESULTS

Based on the transcriptome expression levels of ICD-related genes, a total of 403 BLCA patients in the TCGA database were divided into two subgroups with different ICD molecular patterns by consensus cluster analysis. These subgroups showed different clinicopathological features, survival outcomes, tumor microenvironment (TME) characteristics, immune-related scores, and treatment response. Moreover, the established prediction model and ICD score can effectively distinguish high risk/score patients from low risk/score patients, which has excellent predictive value. Finally, we found that the key gene HSP90AA1 was highly expressed in the high-ICD score group and in bladder cancer tissues, and was confirmed to be associated with the proliferation of bladder cancer cells.

CONCLUSION

To sum up, we established a new classification system for BLCA based on ICD-related genes. This stratification has significant predictive power for clinical outcomes and can effectively evaluate the prognosis and immunotherapy of BLCA patients. Finally, it was proved that HSP90AA1 was highly expressed in BLCA and would be a promising therapeutic target for BLCA.

Integration analysis based on fatty acid metabolism robustly predicts prognosis, dissecting immunity microenvironment and aiding immunotherapy for soft tissue sarcoma

Background: Soft tissue sarcoma (STS) is a highly malignant tumor with a dismal prognosis. Presently, the dysregulation of fatty acid metabolism has received increasing attention in tumor research, but fewer reports are relevant to STS.

Methods: Based on fatty acid metabolism-related genes (FRGs), a novel risk score for STS was developed utilizing univariate analysis and least absolute shrinkage selection operator (LASSO) Cox regression analyses in the STS cohort, which were further validated using the external validation cohort from other databases. Furthermore, independent prognostic analysis, C-index, ROC curves, and nomogram were carried out to investigate the predictive performance of fatty acid-related risk scores. We also analysed the differences in enrichment pathways, the immune microenvironment, gene mutations, and immunotherapy response between the two distinct fatty acid score groups. Moreover, the real-time quantitative polymerase chain reaction (RT-qPCR) was used to further verify the expression of FRGs in STS.

Results: A total of 153 FRGs were retrieved in our study. Next, a novel fatty acid metabolism-related risk score (FAS) was constructed based on 18 FRGs. The predictive performance of FAS was also verified in external cohorts. In addition, the independent analysis, C-index, ROC curve, and nomograph also revealed that FAS could serve as an independent prognostic factor for the STS patients. Meanwhile, our results demonstrated that the STS cohort in two distinct FAS groups had different copy number variations, immune cell infiltration, and immunotherapy responses. Finally, the in vitro validation results demonstrated that several FRGs included in the FAS exhibited abnormal expression in STS.

Conclusion: Altogether, our work comprehensively and systematically clarifies fatty acid metabolism’s potential roles and clinical significance in STS. The novel individualized score based on fatty acid metabolism may be provided as a potential marker and treatment strategy in STS.

An angiogenesis-related three-long non-coding ribonucleic acid signature predicts the immune landscape and prognosis in hepatocellular carcinoma

The tumour microenvironment is a key determinant of the efficacy of immunotherapy. Angiogenesis is closely linked to tumour immunity. We aimed to screen long non-coding ribonucleic acids (lncRNAs) associated with angiogenesis to predict the prognosis of individuals with hepatocellular carcinoma (HCC) and characterise the tumour immune microenvironment (TIME). Patient data, including transcriptome and clinicopathological parameters, were retrieved from The Cancer Genome Atlas database. Moreover, co-expression algorithm was utilized to obtain angiogenesis-related lncRNAs. Additionally, survival-related lncRNAs were identified using Cox regression and the least absolute shrinkage and selection operator algorithm, which aided in constructing an angiogenesis-related lncRNA signature (ARLs). The ARLs was validated using Kaplan-Meier method, time-dependent receiver operating characteristic analyses, and Cox regression. Additionally, an independent external HCC dataset was used for further validation. Then, gene set enrichment analysis, immune landscape, and drug sensitivity analyses were implemented to explore the role of the ARLs. Finally, cluster analysis divided the entire HCC dataset into two clusters to distinguish different subtypes of TIME. This study provides insight into the involvement of angiogenesis-associated lncRNAs in predicting the TIME characteristics and prognosis for individuals with HCC. Furthermore, the developed ARLs and clusters can predict the prognosis and TIME characteristics in HCC, thereby aiding in selecting the appropriate therapeutic strategies involving immune checkpoint inhibitors and targeted drugs.

USP51/ZEB1/ACTA2 axis promotes mesenchymal phenotype in gastric cancer and is associated with low cohesion characteristics

poorly cohesive (PC) gastric cancer (GC) (PC-GC) is a distinct histological subtype of GC and is defined as a tumor consisting of isolated or small clusters of tumor cells with poorly differentiated and metastatic characteristics. According to multiple studies, PC-GC is intrinsically heterogeneous, with mesenchymal variants being the most aggressive. However, to date, the molecular mechanisms associated with PC-GC are still not fully understood. This study investigated the role of the USP51/ZEB1/ACTA2 axis in promoting GC metastasis. Single-cell sequencing revealed that E-box binding homeobox 1 (ZEB1) expression was significantly increased in a subpopulation of low-adherent cells and was an independent prognostic factor in GC patients. Furthermore, the bulk transcriptome analysis revealed a significant positive correlation between Ubiquitin Specific Peptidase 51 (USP51), ZEB1, and Actin Alpha 2 (ACTA2), and our data further confirmed that all three were highly co-localized in PC-GC tissues. According to the findings of in vitro and in vivo experiments, USP51 was able to maintain ZEB1 expression to promote ACTA2 transcription, thereby activating the mesenchymal phenotype of GC cells and promoting tumor metastasis. Moreover, USP51 could recruit and activate stromal cells, including M2-like macrophages and fibroblasts, through cancer cells. Clinical data suggested that overexpression of USP51 predicts that patients have difficulty benefiting from immunotherapy and is associated with immune-exclusion tumor characteristics. Collectively, the findings of this study shed light on a key mechanism by which elevated USP51 expression induces Epithelial-mesenchymal transition (EMT) in GC cells, hence facilitating GC cell proliferation, survival, and dissemination. In this view, USP51/ZEB1/ACTA2 may serve as a candidate therapeutic target against GC metastasis.

Angiogenesis-related gene signatures reveal the prognosis of cervical cancer based on single cell sequencing and co-expression network analysis

Cervical cancer ranks first in female reproductive tract tumors in terms of morbidity and mortality. Yet the curative effect of patients with persistent, recurrent or metastatic cervical cancer remains unsatisfactory. Although antitumor angiogenic drugs have been recommended as the first-line treatment options for cervical cancer, there are no comprehensive prognostic indicators for cervical cancer based on angiogenic signature genes. In this study, we aimed to develop a model to assess the prognosis of cervical cancer based on angiogenesis-related (AG) signature genes, and to provide some reference for the comprehensive treatment of cervical cancer in the clinical setting. First we screened the AG gene set from GeneCard website, and then performed angiogenesis-related scores (AGS) per cell from single cell sequencing dataset GSE168652, followed by performing weighted gene co-expression network analysis (WGCNA) for cervical cancer patients according to angiogenesis phenotype. Thus, we established a prognostic model based on AGS by taking the intersection of WGCNA angiogenic module gene and differential gene (DEGs) of GSE168652. The GSE44001 was selected as an external validation set, followed by performing ROC curve analysis to assess its accuracy. The results showed that we successfully constructed a prognostic model related to the AG genes. Patients in the high-AGS group in both the train, test and the validation sets had a worse prognosis than those in the low-AGS group, had lower expression of most immune checkpoint-associated genes and lower tumor mutational burden as well. Patients in the low-AGS group were more sensitive to AMG.706, Bosutinib, and Lenalidomide while Imatinib, Pazopanib, and Sorafenib were more recommended to patients in the high-AGS group. Finally, TXNDC12 and ZC3H13, which have high hazard ratio and poor prognosis in the model, were highly expressed in cervical cancer cell lines and tissue. Meanwhile, the results showed that TXNDC12 promoted the migration of cervical cancer cells and the tubule-forming ability of endothelial cells. In conclusion, our model based on genes with AG features can effectively assess the prognosis of cervical cancer, and can also provide reference for clinicians to choose immune-related treatments.

Cell Pair Algorithm-Based Immune Infiltrating Cell Signature for Improving Outcomes and Treatment Responses in Patients with Hepatocellular Carcinoma

BACKGROUND

Immune interactions play important roles in the regulation of T cells' cytotoxic function, further impacting the anti-tumor efficacy of immunotherapy. A comprehensive analysis of immune cell types in HCC and immune-cell-related signatures predicting prognosis and monitoring immunotherapy efficacy is still absent.

METHODS

More than 1,300 hepatocellular carcinomas (HCC) patients were collected from public databases and included in the present study. The ssGSEA algorithm was applied to calculate the infiltration level of 28 immunocyte subpopulations. A cell pair algorithm was applied to construct an immune-cell-related prognostic index (ICRPI). Survival analyses were performed to measure the survival difference across ICRPI risk groups. Spearman's correlation analyses were used for the relevance assessment. A Wilcoxon test was used to measure the expression level's differences.

RESULTS

In this study, 28 immune subpopulations were retrieved, and 374 immune cell pairs (ICPs) were established, 38 of which were picked out by the least absolute shrinkage and selection operator (LASSO) algorithm. By using the selected ICPs, the ICRPI was constructed and validated to play crucial roles in survival stratification and dynamic monitoring of immunotherapy effect. We also explored several candidate drugs targeting ICRPI. A composite ICRPI and clinical prognostic index (ICPI) was then constructed, which achieved a more accurate estimation of HCC's survival and is a better choice for prognosis predictions in HCC.

CONCLUSIONS

In conclusion, we constructed and validated ICRPI based on the cell pair algorithm in this study, which might provide some novel insights for increasing the survival estimation and clinical response to immune therapy for individual HCC patients and contribute to the personalized precision immunotherapy strategy of HCC.

Characteristic of molecular subtype based on lysosome-associated genes reveals clinical prognosis and immune infiltration of gastric cancer

Background

Lysosome are involved in nutrient sensing, cell signaling, cell death, immune responses and cell metabolism, which play an important role in the initiation and development of multiple tumors. However, the biological function of lysosome in gastric cancer (GC) has not been revealed. Here, we aim to screen lysosome-associated genes and established a corresponding prognostic risk signature for GC, then explore the role and underlying mechanisms.

Methods

The lysosome-associated genes (LYAGs) were obtained from MSigDB database. Differentially expressed lysosome-associated genes (DE-LYAGs) of GC were acquired based on the TCGA database and GEO database. According to expression profiles of DE-LYAGs, we divided the GC patients into different subgroups and then explored tumor microenvironment (TME) landscape and immunotherapy response in LYAG subtypes using GSVA, ESTIMATE and ssGSEA algorithms. Univariate Cox regression analysis, LASSO algorithm and multivariate Cox regression analysis were adopted to identify the prognostic LYAGs and then establish a risk model for patients with GC. The Kaplan-Meier analysis, Cox regression analysis and ROC analysis were utilized to evaluate the performance of the prognostic risk model. Clinical GC specimens were also used to verify the bioinformatics results by qRT-PCR assay.

Results

Thirteen DE-LYAGs were obtained and utilized to distinguish three subtypes in GC samples. Expression profiles of the 13 DE-LYAGs predicted prognosis, tumor-related immunological abnormalities and pathway dysregulation in these three subtypes. Furthermore, we constructed a prognostic risk model for GC based on DEG in the three subtypes. The Kaplan-Meier analysis suggested that higher risk score related to short OS rate. The Cox regression analysis and ROC analysis indicated that risk model had independent and excellent ability in predicting prognosis of GC patients. Mechanistically, a remarkable difference was observed in immune cell infiltration, immunotherapy response, somatic mutation landscape and drug sensitivity. qRT-PCR results showed that compared with corresponding adjacent normal tissues, most screened genes showed significant abnormal expressions and the expression change trends were consistent with the bioinformatics results.

Conclusions

We established a novel signature based on LYAGs which could be served as a prognostic biomarker for GC. Our study might provide new insights into individualized prognostication and precision treatment for GC.

SUMOylation patterns and signature characterize the tumor microenvironment and predict prognosis in lung adenocarcinoma

Background: Recent studies have revealed that SUMOylation modifications are involved in various biological processes, including cancer development and progression. However, the precise role of SUMOylation in lung adenocarcinoma (LUAD), especially in the tumor immune microenvironment, is not yet clear. Methods: We identified SUMOylation patterns by unsupervised consensus clustering based on the expression of SUMOylation regulatory genes. The tumor microenvironment in lung adenocarcinoma was analyzed using algorithms such as GSVA and ssGSEA. Key genes of SUMOylation patterns were screened for developing a SUMOylation scoring model to assess immunotherapy and chemotherapy responses in lung adenocarcinoma patients. Experiments were conducted to validate the differential expression of model genes in lung adenocarcinoma. Finally, we constructed a nomogram based on the SUMOylation score to assess the prognosis of individual lung adenocarcinoma patients. Results: Two patterns of SUMOylation were identified, namely, SUMO-C1, which showed anti-tumor immune phenotype, and SUMO-C2, which showed immunosuppressive phenotype. Different genomic subtypes were also identified; subtype gene-T1 exhibited a reciprocal restriction between the immune microenvironment and stromal microenvironment. High SUMOylation scores were indicative of poor lung adenocarcinoma prognosis. SUMOylation score was remarkably negatively correlated with the infiltration of anti-tumor immune cells, and significantly positively correlated with immune cells promoting immune escape and immune suppression. In addition, patients with low scores responded better to immunotherapy. Therefore, the developed nomogram has a high prognostic predictive value. Conclusion: The SUMOylation patterns can well discriminate the tumor microenvironment features of lung adenocarcinoma, especially the immune cell infiltration status. The SUMOylation score can further assess the relationship between SUMOylation and immune cell crosstalk and has significant prognostic value and can be used to predict immunotherapy and chemotherapy response in patients with lung adenocarcinoma.

A telomerase regulation-related lncRNA signature predicts prognosis and immunotherapy response for gastric cancer

BACKGROUND

Telomeres are involved in the development and progression of gastric cancer (GC). However, the association of telomerase regulation-related lncRNAs with prognosis and immunotherapy responsiveness in gastric cancer is unclear.

METHODS

This study systematically evaluated the relationship between lncRNAs co-expressed with 67 telomerase regulatory genes and gastric cancer prognosis. The risk scores of the samples were calculated based on telomerase regulation-related lncRNAs with prognostic value, and the samples were classified into high-/low-risk groups. The prognostic value of risk groups was then evaluated, a GC prognostic prediction model based on risk groups and clinical characteristics was established, and the prediction accuracy of the model was clarified by receiving operating characteristic (ROC) curves and calibration curves. Finally, the value of risk grouping in GC immunotherapy sensitivity was predicted by comparing MSI status and tumor mutation load between the high- and low-risk groups.

RESULTS

We identified 13 lncRNAs with prognostic value co-expressed with telomerase regulatory genes and observed that the prognosis of the low-risk group was significantly better than that of the high-risk group. Meanwhile, a GC overall survival (OS) prediction model based on risk grouping and clinical characteristics was developed, and ROC curves and calibration curves confirmed the good predictive ability of the model. In addition, the low-risk group exhibited a higher tumor mutation load and MSI-H, suggesting a possible benefit of immunotherapy.

CONCLUSION

We found that telomerase regulation-related lncRNAs have prognostic value in GC patients and contribute to the exploration of more effective immunotherapeutic strategies.

Identification of a Two-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival in Diffuse-Type Gastric Cancer

BACKGROUND

It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice.

METHODS

We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15).

RESULTS

A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset.

CONCLUSIONS

We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.

Crosstalk of angiogenesis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in colorectal adenocarcinoma

Background

Colorectal adenocarcinoma (COAD) is one of the most common malignancies and angiogenesis is vital to the development of cancer. Here, we explored the roles of angiogenesis-related genes (ARGs) that affect the prognosis of COAD and constructed risk models to assess patient prognosis, immune characteristics, and treatment outcomes.

Methods

We comprehensively characterized the transcriptional and genetic modifications of 48 ARGs in COAD and evaluated the expression patterns. We identified two ARG subgroups using the consensus clustering algorithm. Based on the differentially expressed genes (DEGs) of two ARG subtypes, we calculated risk score, namely ARG_scores, and calssified COAD patients into different risk groups. To investigate the expression of ARG_score-related genes, qRT-PCR was performed. Subsequently, we mapped the nomogram to visually and accurately describe the value of the application of ARG_score. Finally, the correlation between ARG_score and clinical features, immune infiltration along with drug sensitivity were explored.

Results

We identified two ARG related subgroups and there were great differences in overall survival (OS) and tumor microenvironment. Then, we created an ARG_score for predicting overall survival based on eight DEGs and confirmed its reliable predictive power in COAD patients, with higher ARG_score associated with worse prognosis. Furthermore, eight ARG_score-related genes expression was investigated by qRT-PCR. To make the ARG_score clinically feasible, we created a highly reliable nomogram. We also found a higher proportion of microsatellite instability-high (MSI-H) and higher tumor mutational burden (TMB) in the high-risk group. In addition, ARG_score was notably correlated with cancer stem cell indices and drug sensitivity.

Conclusion

This scoring model has potential clinical application value in the prognosis, immune microenvironment and therapeutic drug sensitivity of COAD, which provides new insights for personalized treatment.

The role of Angiogenesis and remodeling (AR) associated signature for predicting prognosis and clinical outcome of immunotherapy in pan-cancer

Background

Angiogenesis and remodeling (AR) is necessary for the growth and metastasis of cancers. Although AR related genes involved in this process are reported, the correlation between AR and clinical outcome, immune cell infiltration, and immunotherapy is still unknown in diverse cancers. This study aimed to investigate the role of AR in the tumor immune microenvironment (TIME) in pan-cancer, and explore its values in prognostic prediction and therapeutic responses.

Methods

Firstly, AR genes (including angiogenesis genes and blood vessel remodeling genes) are collected from MsigDB database. The differential expression, and prognostic value of AR genes were studied in 33 tumor types based on TCGA and GTEx data. The AR score of each sample was calculated using the "ssGSEA" function of R package "GSVA" in pan-cancer. The correlation of the AR score with TIME index, such as the amount of stromal and immune components and the immune cell infiltration, was evaluated via integrating multiple computational methods. And we also utilized IMvigor210 and GSE78220 data to explore the prediction value of the AR score on the immunotherapy response.

Results

Significant differences in AR gene expression between tumors and adjacent normal tissues were found in most cancer types. The AR score varied depending on the types of tumors, and high score was related to worse survival in various tumors, such as pancreatic and stomach adenocarcinoma and so on. Moreover, the AR score was further explored to be positively correlated with proportions and pathways of immune and stromal in TIME. And the AR score was positively correlated with immunosuppressive cells, including TAMs and iTregs, while negatively with CD8+ T cells. Further analysis revealed that patients with high AR had worse therapy efficacy and survival status in bladder cancer and melanomas.

Conclusions

Our systematic analysis revealed that AR is closely associated TIME, and prognosis, and clinical characteristics in multiple cancers. Targeting AR genes may activate immune microenvironment and increase the efficacy of immunotherapy.

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.

The prognostic value and immunological role of angiogenesis-related patterns in colon adenocarcinoma

Colon adenocarcinoma (COAD) is a malignant tumor with a high mortality rate. Angiogenesis plays a key role in the development and progression of cancer. However, in COAD, studies between angiogenesis and prognosis, immune cell infiltration, and personalized treatment guidance are currently lacking. In the present study, we comprehensively assessed 35 angiogenesis-related genes (ARG) and identified key ARGs affecting OS in COAD patients. The ARG Prognostic Index (ARGPI) was constructed based on a univariate Cox regression model and its prognostic value was evaluated in TCGA-COAD, GSE39582, GSE161158 and TRSJTUSM Cohort. We constructed ARGPI as an independent risk factor for OS in COAD patients and combined with clinical parameters to further construct an ARGPI-based nomogram, which showed a strong ability to predict overall survival in COAD patients. High ARGPI is associated with cancer-related and immune-related biological processes and signaling pathways; high TP53 mutation rate; high infiltration of MSC, pericytes, and stromal cells; and more CMS4 subtype. And low ARGPI benefited more from immune checkpoint inhibitor treatment. In addition, we also predicted the sensitivity of different ARGPI groups to common chemotherapeutic and targeted agents. In conclusion, this study constructed an ARGPI based on ARG, which robustly predicted the OS of COAD patients and provided a possible personalized treatment regime for COAD patients.

A novel angiogenesis-based molecular signature related to prognosis and tumor immune interactions of pancreatic cancer

Angiogenesis, a hallmark of cancer, is related to prognosis, tumor progression, and treatment response. Nevertheless, the correlation of angiogenesis-based molecular signature with clinical outcome and immune cell infiltration has not been thoroughly studied in pancreatic cancer. In this study, multiple bioinformatics methods were combined to evaluate prognosis, immune cell infiltration, and the alterations of angiogenesis-related genes (ARGs) in PC samples, and further establish a novel angiogenesis-related gene signature. Moreover, the protein and mRNA expression levels of four angiogenesis risk genes were determined by Human Protein Atlas (HPA) database and qPCR analysis, respectively. Here, we recognized two distinct angiogenesis subtypes and two gene subtypes, and revealed the critical roles of ARGs in the tumor immune microenvironment (TIME), clinical features, and prognosis. Consequently, we established an ARGs score to predict prognosis and therapeutic response of PC patients, and validated its robust predictive ability. Additionally, the ARGs score was markedly associated with clinical outcomes, tumor mutation burden (TMB), and chemotherapeutic drug sensitivity. In brief, our findings imply that the ARGs score is a robust prognostic indicator and may contribute to the development of effective individualized therapies for PC.

Molecular subtypes, clinical significance, and tumor immune landscape of angiogenesis-related genes in ovarian cancer

Angiogenesis is a physiological process, where new blood vessels are formed from pre-existing vessels through the mechanism called sprouting. It plays a significant role in supporting tumor growth and is expected to provide novel therapeutic ideas for treating tumors that are resistant to conventional therapies. We investigated the expression pattern of angiogenesis-related genes (ARGs) in ovarian cancer (OV) from public databases, in which the patients could be classified into two differential ARG clusters. It was observed that patients in ARGcluster B would have a better prognosis but lower immune cell infiltration levels in the tumor microenvironment. Then ARG score was computed based on differentially expressed genes via cox analysis, which exhibited a strong correlation to copy number variation, immunophenoscore, tumor mutation load, and chemosensitivity. In addition, according to the median risk score, patients were separated into two risk subgroups, of which the low-risk group had a better prognosis, increased immunogenicity, and stronger immunotherapy efficacy. Furthermore, we constructed a prognostic nomogram and demonstrated its predictive value. These findings help us better understand the role of ARGs in OV and offer new perspectives for clinical prognosis and personalized treatment.

Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma

Background: Cellular senescence is a novel hallmark of cancer associated with patient outcomes and tumor immunotherapy. However, the value of cellular senescence-related long non-coding RNAs (lncRNAs) in predicting prognosis and immunotherapy response for stomach adenocarcinoma (STAD) patients needs further investigation.

Methods: The transcriptome and corresponding clinical information of STAD and cellular senescence-related genes were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and CellAge databases. Differential expression analysis and coexpression analysis were performed to obtain cellular senescence-related lncRNAs. Univariate regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were conducted to establish the cellular senescence-related lncRNA prognostic signature (CSLPS). Next, the survival curve, ROC curve, and nomogram were developed to assess the capacity of predictive models. Moreover, principal component analysis (PCA), gene set enrichment analysis (GSEA), tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE) score analysis were performed between high- and low-risk groups.

Results: A novel CSLPS involving fifteen lncRNAs (REPIN1-AS1, AL355574.1, AC104695.3, AL033527.2, AC083902.1, TYMSOS, LINC00460, AC005165.1, AL136115.1, AC007405.2, AL391152.1, SCAT1, AC129507.1, AL121748.1, and ADAMTS9-AS1) was developed. According to the nomogram, the risk model based on the CSLPS was an independent prognostic factor and could predict 1-, 3-, and 5-year overall survival for STAD patients. GSEA suggested that the high-risk group was mainly associated with Toll-like receptor, JAK/STAT, NOD-like receptor, and chemokine signaling pathways. Further analysis revealed that STAD patients in the low-risk group with better clinical outcomes had a higher TMB, higher proportion of high microsatellite instability (MSI-H), better immune infiltration, and lower TIDE scores.

Conclusion: A fifteen-CSlncRNA prognostic signature could predict survival outcomes, and patients in the low-risk group may be more sensitive to immunotherapy.

The clinical significance, immune infiltration, and tumor mutational burden of angiogenesis-associated lncRNAs in kidney renal clear cell carcinoma

Background

Poor prognosis of kidney renal clear cell carcinoma (KIRC) is often related to angiogenesis. The lncRNAs that regulate angiogenesis could also affect the prognosis of KIRC. It is meaningful for us to use lncRNAs related to angiogenesis to construct a generic, individualized prognostic signature for patients with KIRC.

Methods

We identified eight angiogenesis-associated genes (AAGs) by differential expression analysis and univariate Cox regression from The Cancer Genome Atlas dataset, including 537 KIRC samples and 72 normal samples. In total, 23 prognostic lncRNAs were screened out after Pearson correlation analysis and univariate Cox regression analysis. Then, we performed least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression to establish a four-AAG-related lncRNA prognostic signature.

Results

The risk score was calculated for each KIRC patients by using a four-AAG-related lncRNA prognostic signature. We divided the KIRC patients into high- and low-risk groups by the median of the risk score. It was confirmed that the AAG-related lncRNA prognostic signature has good prognostic value for KIRC patients by time-dependent receiver operating characteristic and Kaplan–Meier survival analysis. We identified 3,399 differentially expressed genes between the high- and low-risk groups and performed their functional enrichment analyses. The AAG-related lncRNA prognostic signature was an independent prognostic predictor for KIRC patients and was used to perform a combined nomogram. We reevaluated them in terms of survival, clinic characteristics, tumor-infiltrating immune cells and tumor mutation burden.

Conclusion

Our research indicates that the AAG-related lncRNA prognostic signature is a promising and potential independent prognostic indicator for KIRC patients. Then, it could offer new insights into the prognosis assessment and potential treatment strategies of KIRC patients.