Comprehensive Molecular Analyses of a Macrophage-Related Gene Signature With Regard to Prognosis, Immune Features, and Biomarkers for Immunotherapy in Hepatocellular Carcinoma Based on WGCNA and the LASSO Algorithm

Dendritic Cell-Related Gene Signatures in Hepatocellular Carcinoma: An Analysis for Prognosis and Therapy Efficacy Evaluation

Background

This study aimed to identify dendritic cells (DCs) related genes in hepatocellular carcinoma (HCC) patients, establish DC-related subtypes and signatures, and correlate them with prognosis and treatment response.

Methods

DC-related genes were screened using Weighted Gene Co-expression Network Analysis (WGCNA) based on RNA sequencing from the TCGA (374 samples), GSE14520 (242 samples), and GSE76427 datasets (115 samples), following immune infiltration assessment by the TIME method. Two DC-related subtypes in HCC were identified through unsupervised clustering. A DC-related signature (DCRS) predictive of overall survival was constructed using LASSO and Cox regression models, and validated across the three datasets. Additionally, genetic mutation characteristics, immune infiltration levels, and treatment sensitivity were explored in DCRS risk groups. The expression levels of DCRS genes and risk scores were validated in the transcriptome of 13 HCC patients receiving combined targeted therapy and immunotherapy in the Guangxi cohort using Wilcoxon test.

Results

A signature consisting of 13 genes related to DCs was constructed, and the superior prognostic consistency of the low DCRS risk group was validated across the TCGA (P=0.003), GSE76427 (P=0.005), and GSE14520 (P=0.047) datasets. Furthermore, in the 147-sample transarterial chemoembolization (TACE) treatment dataset GSE104580, the response group exhibited lower risk scores than the non-response group (P=0.01), whereas in the 140-sample Sorafenib treatment dataset GSE109211 (P=0.041) and the 17-sample anti-PD-1 treatment dataset GSE202069 (P=0.027), the risk scores were higher in the response group. We also validated the gene expression levels of DCRS and the higher risk scores in the response group of the Guangxi cohort (P=0.034).

Conclusion

A DCRS consisting of 13 genes was established in HCC, facilitating the prediction of patient prognosis and responsiveness to TACE, targeted therapy, and immunotherapy.

Identification of two heterogeneous subtypes of hepatocellular carcinoma with distinct pathway activities and clinical outcomes based on gene set variation analysis

Background

High heterogeneity is an essential feature of malignant tumors. This study aims to reveal the drivers of hepatocellular carcinoma heterogeneity for prognostic stratification and to guide individualized treatment.

Methods

Omics data and clinical data for two HCC cohorts were derived from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Atlas (ICGC), respectively. CNV data and methylation data were downloaded from the GSCA database. GSVA was used to estimate the transcriptional activity of KEGG pathways, and consensus clustering was used to categorize the HCC samples. The pRRophetic package was used to predict the sensitivity of samples to anticancer drugs. TIMER, MCPcounter, quanTIseq, and TIDE algorithms were used to assess the components of TME. LASSO and COX analyses were used to establish a prognostic gene signature. The biological role played by genes in HCC cells was confirmed by in vitro experiments.

Results

We classified HCC tissues into two categories based on the activity of prognostic pathways. Among them, the transcriptional profile of cluster A HCC is similar to that of normal tissue, dominated by cancer-suppressive metabolic pathways, and has a better prognosis. In contrast, cluster B HCC is dominated by high proliferative activity and has significant genetic heterogeneity. Meanwhile, cluster B HCC is often poorly differentiated, has a high rate of serum AFP positivity, is prone to microvascular invasion, and has shorter overall survival. In addition, we found that mutations, copy number variations, and aberrant methylation were also crucial drivers of the differences in heterogeneity between the two HCC subtypes. Meanwhile, the TME of the two HCC subtypes is also significantly different, which offers the possibility of precision immunotherapy for HCC patients. Finally, based on the prognostic value of molecular subtypes, we developed a gene signature that could accurately predict patients' OS. The riskscore quantified by the signature could evaluate the heterogeneity of HCC and guide clinical treatment. Finally, we confirmed through in vitro experiments that RFPL4B could promote the progression of Huh7 cells.

Conclusion

The molecular subtypes we identified effectively exposed the heterogeneity of HCC, which is important for discovering new effective therapeutic targets.

Computed tomography-based radiomics predicts the fibroblast-related gene EZH2 expression level and survival of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. The primary treatment strategies for HCC currently include liver transplantation and surgical resection. However, these methods often yield unsatisfactory outcomes, leading to a poor prognosis for many patients. This underscores the urgent need to identify and evaluate novel therapeutic targets that can improve the prognosis and survival rate of HCC patients.

AIM

To construct a radiomics model that can accurately predict the EZH2 expression in HCC.

METHODS

Gene expression, clinical parameters, HCC-related radiomics, and fibroblast-related genes were acquired from public databases. A gene model was developed, and its clinical efficacy was assessed statistically. Drug sensitivity analysis was conducted with identified hub genes. Radiomics features were extracted and machine learning algorithms were employed to generate a radiomics model related to the hub genes. A nomogram was used to illustrate the prognostic significance of the computed Radscore and the hub genes in the context of HCC patient outcomes.

RESULTS

EZH2 and NRAS were independent predictors for prognosis of HCC and were utilized to construct a predictive gene model. This model demonstrated robust performance in diagnosing HCC and predicted an unfavorable prognosis. A negative correlation was observed between EZH2 expression and drug sensitivity. Elevated EZH2 expression was linked to poorer prognosis, and its diagnostic value in HCC surpassed that of the risk model. A radiomics model, developed using a logistic algorithm, also showed superior efficiency in predicting EZH2 expression. The Radscore was higher in the group with high EZH2 expression. A nomogram was constructed to visually demonstrate the significant roles of the radiomics model and EZH2 expression in predicting the overall survival of HCC patients.

CONCLUSION

EZH2 plays significant roles in diagnosing HCC and therapeutic efficacy. A radiomics model, developed using a logistic algorithm, efficiently predicted EZH2 expression and exhibited strong correlation with HCC prognosis.

Machine learning developed a macrophage signature for predicting prognosis, immune infiltration and immunotherapy features in head and neck squamous cell carcinoma

Macrophages played an important role in the progression and treatment of head and neck squamous cell carcinoma (HNSCC). We employed weighted gene co-expression network analysis (WGCNA) to identify macrophage-related genes (MRGs) and classify patients with HNSCC into two distinct subtypes. A macrophage-related risk signature (MRS) model, comprising nine genes: IGF2BP2, PPP1R14C, SLC7A5, KRT9, RAC2, NTN4, CTLA4, APOC1, and CYP27A1, was formulated by integrating 101 machine learning algorithm combinations. We observed lower overall survival (OS) in the high-risk group and the high-risk group showed elevated expression levels in most of the immune checkpoint and human leukocyte antigen (HLA) genes, suggesting a strong immune evasion capacity. Correspondingly, TIDE score positively correlated with risk score, implying that high-risk tumors may resist immunotherapy more effectively. At the single-cell level, we noted macrophages in the tumor microenvironment (TME) predominantly stalled in the G2/M phase, potentially hindering epithelial-mesenchymal transition and playing a crucial role in the inhibition of tumor progression. Finally, the proliferation and migration abilities of HNSCC cells significantly decreased after the expression of IGF2BP2 and SLC7A5 reduced. It also decreased migration ability of macrophages and facilitated their polarization towards the M1 direction. Our study constructed a novel MRS for HNSCC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy for HNSCC patients.

Co-Expression Network Analysis and Molecular Docking Demonstrate That Diosgenin Inhibits Gastric Cancer Progression via SLC1A5/mTORC1 Pathway

Background

Tumor-Node-Metastasis (TNM) stage of gastric cancer (GC) is one of the main factors affecting clinical outcome. The aim of this study was to explore the targets related to TNM stage of GC, and screening natural bioactive drug.

Methods

RNA sequencing data of the TCGA-STAD cohort were downloaded from UCSC database. Genes associated with TNM staging were identified by weighted gene co-expression network analysis (WGCNA). Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (Xgboost), random forest (RF) and cytohubba plug-in of cytoscope were applied to screen hub genes. Natural bioactive ingredients were available from the HERB database. Molecular docking was used to evaluate the binding activity of active ingredients to the hub protein. CCK-8, flow cytometry, transwell and Western blot assays were used to analyze the effects of diosgenin on GC cells.

Results

898 TNM-related genes were screened out through WGCNA. Three genes associated with GC progression/prognosis were identified, including nuclear receptor subfamily 3 group C member 2 (NR3C2), solute carrier family 1 member 5 (SLC1A5) and FAT atypical cadherin 1 (FAT1) based on the machine learning algorithms and hub co-expression network analysis. Diosgenin had good binding activity with SLC1A5. SLC1A5 was highly expressed in GC and was closely associated with tumor stage, overall survival and immune infiltration of GC patients. Diosgenin could inhibit cell viability and invasive ability, promote apoptosis and induce cell cycle arrest in G0/G1 phase. In addition, diosgenin promoted cleaved caspase 3 expression and inhibited Ki67, cyclin D1, p-S6K1, and SLC1A5 expression levels, while the mTORC1 activator (MHY1485) reversed this phenomenon.

Conclusion

For the first time, this work reports diosgenin may inhibit the activation of mTORC1 signaling through targeting SLC1A5, thereby inhibiting the malignant behaviors of GC cells.

Molecular subtype identification and prognosis stratification by a immunogenic cell death-related gene expression signature in colorectal cancer

OBJECTIVES

This study intended to develop a new immunogenic cell death (ICD)-related prognostic signature for colorectal cancer (CRC) patients.

RESEARCH DESIGN AND METHODS

The Non-Negative Matrix Factorization (NMF) algorithm was adopted to cluster tumor samples based on ICD gene expression to obtain ICD-related subtypes. Survival analysis and immune microenvironment analysis were conducted among different subtypes. Regression analysis was used to construct the model. Based on riskscore median, cancer patients were classified into high and low risk groups, and independent prognostic ability of the model was analyzed. The CIBERSORT algorithm was adopted to determine the immune infiltration level of both groups.

RESULTS

We analyzed the differential genes between cluster 4 and cluster 1-3 and obtained 12 genes with the best prognostic features finally (NLGN1, SLC30A3, C3orf20, ADAD2, ATOH1, ATP6V1B1, KCNQ2, MUCL3, RGCC, CLEC17A, COL6A5, and INSL4). In addition, patients with lower risk had higher levels of infiltration of most immune cells, lower Tumor Immune Dysfunction and Exclusion (TIDE) level and higher immunophenscore (IPS) level than those with higher risk.

CONCLUSIONS

This study constructed and validated the ICD feature signature predicting CRC prognosis and provide a reference criteria for guiding the prognosis and immunotherapy of CRC cancer patients.

Prognostic implication and immunotherapy response prediction of a novel ubiquitination-related gene signature in liver cancer

HCC, also known as hepatocellular carcinoma, is a frequently occurring form of cancer with an unfavorable prognosis. This research constructed a prognostic signature related to ubiquitination and investigated its correlation with the response to immunotherapy in HCC. The Molecular Signatures Database provided a compilation of genes associated with ubiquitination. A gene signature related to ubiquitination was obtained through Cox regression using the Least Absolute Shrinkage and Selection Operator method. The genetic factors CPY26B1, MCM10, SPINK4, and TRIM54 notably impacted the outcomes of HCC. The patients were divided into two groups: one group had a high risk of poor survival while the other had a low risk but a greater chance of controlling HCC progression. Both univariate and multivariate analyses using Cox regression found the risk score to be an independent predictor of HCC prognosis. Gene set enrichment analysis (GSEA) indicated enrichment in cell cycle and cancer-related microRNAs in high-risk groups. The tumor microenvironment (TME), response to immunotherapy, and effectiveness of chemotherapy medications positively correlated with the risk score. In the high-risk group, erlotinib showed higher IC50 values compared to the low-risk group which exhibited higher IC50 values for VX-11e, AKT inhibitor VIII, AT-7519, BMS345541, Bortezomib, CP466722, FMK, and JNK-9L. The results of RT-qPCR revealed that the expression of four UEGs was higher in tumor tissue as compared to normal tissue. Based on the genes that were expressed differently and associated with ubiquitination-related tumor categorization, we have developed a pattern of four genes and a strong nomogram that can predict the prognosis of HCC, which could be useful in identifying and managing HCC.

Application of artificial intelligence in the prediction of immunotherapy efficacy in hepatocellular carcinoma: Current status and prospects

Artificial Intelligence (AI) has increased as a potent tool in medicine, with promising oncology applications. The emergence of immunotherapy has transformed the treatment terrain for hepatocellular carcinoma (HCC), offering new hope to patients with this challenging malignancy. This article examines the role and future of AI in forecasting the effectiveness of immunotherapy in HCC. We highlight the potential of AI to revolutionize the prediction of therapy response, thus improving patient selection and clinical outcomes. The article further outlines the challenges and future research directions in this emerging field.

POLE -related gene signature predicts prognosis, immune feature, and drug therapy in human endometrioid carcinoma

The POLE subtype of Endometrial carcinoma (EC) is linked to a favourable prognosis in the molecular classification. We proposed to ascertain the potential connection between the POLE subtype and improved prognosis. In order to forecast the prognosis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis and weighted gene co-expression network analysis (WGCNA) were employed, and a POLE-related risk signature (PRS) model was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) with the "GSVA" package was employed to analyse immunity characteristics. Drug susceptibility studies were conducted to compare the half-maximal inhibitory concentration (IC50) of medicines between high- and low-risk groups. The PRS model was generated employing the LASSO Cox regression coefficients of the ELF1, MMADHC, andAL021707.6 genes. Our study demonstrated that the risk score was linked to tumour stage, grade, and survival. Furthermore, the low-risk group possessed elevated levels of gene expression connected with immunological checkpoints and HLA. Our outcomes emerged that the PRS model might have value in identifying patients with a good prognosis and in facilitating personalised treatment in the clinic.

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Objective

Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.

Methods

We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.

Results

We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.

Conclusion

The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer

BACKGROUND

The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI.

METHODS

Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes.

RESULTS

This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells.

CONCLUSION

We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

Identification of Lower Grade Glioma Antigens Based on Ferroptosis Status for mRNA Vaccine Development

Purpose

mRNA vaccines represent a promising and innovative strategy within the realm of cancer immunotherapy. However, their efficacy in treating lower-grade glioma (LGG) requires evaluation. Ferroptosis exhibits close associations with the initiation, evolution, and suppression of cancer. In this study, we explored the landscape of the ferroptosis-associated tumor microenvironment to facilitate the development of mRNA vaccines for LGG patients.

Patients and Methods

Genomic and clinical data of the LGG patients was obtained from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Ferroptosis-related tumor antigens were identified based on differential expression, mutation status, correlation with antigen-presenting cells, and prognosis, relevance to immunogenic cell death (ICD). Antigen expression levels in LGG specimens and cell lines were validated using real time-polymerase chain reaction (RT-PCR). Consensus clustering was employed for patient classification. The immune landscapes of ferroptosis subtypes were further characterized, including immune responses, prognostic ability, tumor microenvironment, and tumor-related signatures.

Results

Five tumor antigens, namely, HOTAIR, IDO1, KIF20A, NR5A2, and RRM2 were identified in LGG. RT-PCR demonstrated higher expression of these genes in LGG compared to the control. Twelve gene modules and four ferroptosis subtypes (FS1-FS4) of LGG were defined. FS2 and FS4, characterized as "cold" tumors due to their decreased tumor mutation burden (TMB) and immune checkpoint proteins (ICPs), were deemed appropriate candidates for the mRNA vaccine.

Conclusion

HOTAIR, IDO1, KIF20A, NR5A2, and RRM2 were identified as promising candidate antigens for the development of an LGG mRNA vaccine, particularly offering potential benefits to FS2 and FS4 patients.

Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy

PURPOSE

Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa.

METHODS

Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features.

RESULTS

Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics.

CONCLUSION

This study aimed to improve clinicians' ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.

Macrophage-Related Gene Signatures for Predicting Prognosis and Immunotherapy of Lung Adenocarcinoma by Machine Learning and Bioinformatics

Background

In recent years, the immunotherapy of lung adenocarcinoma has developed rapidly, but the good therapeutic effect only exists in some patients, and most of the current predictors cannot predict it very well. Tumor-infiltrating macrophages have been reported to play a crucial role in lung adenocarcinoma (LUAD). Thus, we want to build novel molecular markers based on macrophages.

Methods

By non-negative matrix factorization (NMF) algorithm and Cox regression analysis, we constructed macrophage-related subtypes of LUAD patients and built a novel gene signature consisting of 12 differentially expressed genes between two subtypes. The gene signature was further validated in Gene-Expression Omnibus (GEO) datasets. Its predictive effect on prognosis and immunotherapy outcome was further evaluated with rounded analyses. We finally explore the role of TRIM28 in LUAD with a series of in vitro experiments.

Results

Our research indicated that a higher LMS score was significantly correlated with tumor staging, pathological grade, tumor node metastasis stage, and survival. LMS was identified as an independent risk factor for OS in LUAD patients and verified in GEO datasets. Clinical response to immunotherapy was better in patients with low LMS score compared to those with high LMS score. TRIM28, a key gene in the gene signature, was shown to promote the proliferation, invasion and migration of LUAD cell.

Conclusion

Our study highlights the significant role of gene signature in predicting the prognosis and immunotherapy efficacy of LUAD patients, and identifies TRIM28 as a potential biomarker for the treatment of LUAD.

Targeting ferroptosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor with complex survival mechanism and drug resistance, resulting in cancer-related high mortality in the world. Ferroptosis represents a form of regulated cell death, typically distinguished by iron-dependent lipid peroxidation. Cancer cells often employ antioxidant defenses to evade the harmful effects of excess iron. Recent research has proposed that directing interventions towards ferroptosis could serve as an effective strategy in curbing the proliferation and invasion of HCC. Immunotherapy has made some preliminary progress in the remodeling of immune microenvironment, but it has not completely inhibited HCC growth, invasion and drug resistance. Furthermore, ferroptosis is widely observed in the formation of immune microenvironment of HCC and mediates the response of many targeted drugs and immunotherapy. Clarifying the role of ferroptosis in these complex processes is expected to provide a new prospect for HCC treatment. In this review, we outline the mechanisms by which HCC develops invasiveness and drug resistance by evading iron-dependent death, and paint a comprehensive landscape of ferroptosis in different cell types in the HCC immune microenvironment.

Weighted gene co-expression network analysis for hub genes in colorectal cancer

BACKGROUND

This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA).

METHODS

Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established.

RESULTS

After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC.

CONCLUSIONS

CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.

Malignant cell receptor-ligand subtypes guide the prediction of prognosis and personalized immunotherapy of liver cancer

OBJECTIVE

Liver cancer is a prevalent disease with a dismal prognosis. The aim of the research is to identify subgroups based on malignant cell receptor ligand gene from single-cell RNA, which might lead to customized immunotherapy for patients with liver cancer.

METHODS

Based on scRNA-seq data, we identified the receptor-ligand genes associated with prognosis and classify patients into molecular subtypes by univariate Cox regression and consensus clustering. LASSO regression was performed to construct a prognostic model, which was validated in TCGA and ICGC datasets. Immune infiltration and prediction of immunotherapy response were analyzed using ssGSEA, ESTIMATE, TIDE, and TRS score calculation. Finally, qPCR and Western blot validation of key genes and protein levels in cell lines.

RESULTS

A risk model using 16-gene expression levels predicted liver cancer patients' prognosis. The RiskScore associated significantly with tumor clinical characteristics and immunity, integrated with clinicopathological features for survival prediction. Differential expression of SRXN1 was verified in hepatocellular carcinoma and normal liver cells.

CONCLUSION

Our study utilizes single-cell analysis to investigate the communication between malignant cells and other cell types, identifying molecular subtypes based on malignant cell receptor ligand genes, offering new insights for the development of personalized immunotherapy and prognostic prediction models.

Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis

OBJECTIVES

To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.

METHODS

The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas (TCGA) database. Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was achieved by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were identified. A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.

RESULTS

B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. The patients were clustered into two groups (Cluster 1 ´ and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1 ´, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). 35 genes related to key immune cells were screened out by WGCNA and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients.

CONCLUSIONS

According to the immune cell infiltration score, bladder cancer patients can be classified. Furthermore, bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.

Diagnostic role and immune correlates of programmed cell death-related genes in hepatocellular carcinoma

Programmed cell death (PCD) is thought to have multiple roles in tumors. Here, the roles of PCD-related genes were comprehensively analyzed to evaluate their values in hepatocellular carcinoma (HCC) diagnosis and prognosis. Gene expression and single-cell data of HCC patients, and PCD-related genes were collected from public databases. The diagnostic and prognostic roles of differentially expressed PCD-related genes in HCC were explored by univariate and multivariate Cox regression analyses. Single-cell data were further analyzed for the immune cells and expression of feature genes. Finally, we evaluated the expression of genes by quantitative real-time polymerase chain reaction and Western blot, and the proportion of immune cells was detected by flow cytometry in HCC samples. We obtained 52 differentially expressed PCD-related genes in HCC, based on which the consensus clustering analysis cluster 2 was found to have a worse prognosis than cluster 1. Then 10 feature genes were identified using LASSO analysis, and programmed cell death index (PCDI) was calculated to divided HCC patients into high-PCDI and low-PCDI groups. Worse prognosis was observed in high-PCDI group. Cox regression analysis showed that PCDI is an independent prognostic risk factor for HCC patients. Additionally, SERPINE1 and G6PD of feature genes significantly affect patient survival. Macrophages and Tregs were significantly positively correlated with PCDI. G6PD mainly expressed in macrophages, SERPINE1 mainly expressed in fibroblast. The experimental results confirmed the high expression of SERPINE1 and G6PD in HCC compared with the control, and the infiltration level of macrophages and Treg in HCC was also obviously elevated. PCDI may be a new predictor for the diagnosis of patients with HCC. The association of SERPINE1 and G6PD with the immune environment will provide new clues for HCC therapy.

Urinary SPP1 has potential as a non-invasive diagnostic marker for focal segmental glomerulosclerosis

Focal segmental glomerulosclerosis (FSGS) is a type of chronic glomerular nephropathy showing characteristic glomerular sclerosis, diagnosed by kidney biopsy. However, it is difficult and expensive to monitor disease progression with repeated renal biopsy in clinical practice, and thus here we explored the feasibility of urine biomarkers as non-invasive diagnostic tools. We downloaded scRNA-seq datasets of 20 urine cell samples and 3 kidney tissues and obtained two gene lists encoding extracellular proteins for bioinformatic analysis; in addition, we identified key EP-Genes by immunohistochemical staining and performed bulk RNA sequencing with 12 urine samples. We report that urine cells and kidney cells were correlated. A total of 64 EP-Genes were acquired by intersecting genes of distal tubular cluster with extracellular proteins. Function enrichment analysis showed that EP-Genes might be involved in the immune response and extracellular components. Six key EP-Genes were identified and correlated with renal function. IMC showed that key EP-Genes were located mainly in tubules. Cross verification and examination of a urine RNAseq dataset showed that SPP1 had diagnostic potential for FSGS. The presence of urine SPP1 was primarily associated with macrophage infiltration in kidney, and the pathogenesis of FSGS may be related to innate immunity. Urinary cells seemed to be strongly similar to kidney cells. In summary, SPP1 levels reflect renal function and may have potential as a biomarker for non-invasive diagnosis of FSGS.

A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning

BACKGROUND

Ovarian cancer ranks the leading cause of gynecologic cancer-related death in the United States and the fifth most common cause of cancer-related mortality among American women. Increasing evidences have highlighted the vital role of macrophages M2/M1 proportion in tumor progression, prognosis and immunotherapy.

METHODS

Weighted gene co-expression network analysis (WGCNA) was performed to identify macrophages related markers. Integrative procedure including 10 machine learning algorithms were performed to develop a prognostic macrophage related signature (MRS) with TCGA, GSE14764, GSE140082 datasets. The role of MRS in tumor microenvironment (TME) and therapy response was evaluated with the data of CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC, GSE91061 and IMvigor210 dataset.

RESULTS

The optimal MRS developed by the combination of CoxBoost and StepCox[forward] algorithm served as an independent risk factor in ovarian cancer. Compared with stage, grade and other established prognostic signatures, the current MRS had a better performance in predicting the overall survival rate of ovarian cancer patients. Low risk score indicated a higher TME score, higher level of immune cells, higher immunophenoscore, higher tumor mutational burden, lower TIDE score and lower IC50 value in ovarian cancer. The survival prediction nomogram had a good potential for clinical application in predicting the 1-, 3-, and 5-year overall survival rate of ovarian cancer patients.

CONCLUSION

All in all, the current study constructed a powerful prognostic MRS for ovarian cancer patients using 10 machine learning algorithms. This MRS could predict the prognosis and drug sensitivity in ovarian cancer.

Tumour-associated macrophages as a potential target to improve natural killer cell-based immunotherapies

Adoptive transfer of natural killer (NK) cells has been proposed as a novel immunotherapy for malignant tumours resistant to current therapeutic modalities. Several clinical studies have demonstrated that the NK cell-infusion is well tolerated without severe side effects and shows promising results in haematological malignancies. However, patients with malignant solid tumours do not show significant responses to this therapy. Such disappointing results largely arise from the inefficient delivery of infused NK cells and the impairment of their functions in the tumour microenvironment (TME). Tumour-associated macrophages (TAMs) are the most abundant stromal cells in the TME of most solid tumours, and a high TAM density correlates with poor prognosis of cancer patients. Although our knowledge of the interactions between TAMs and NK cells is limited, many studies have indicated that TAMs suppress NK cell cytotoxicity against cancer cells. Therefore, blockade of TAM functions can be an attractive strategy to improve NK cell-based immunotherapies. On the other hand, macrophages are reported to activate NK cells under certain circumstances. This essay presents our current knowledge about mechanisms by which macrophages regulate NK cell functions and discusses possible therapeutic approaches to block macrophage-mediated NK cell suppression.

Unleashing the efficacy of immune checkpoint inhibitors for advanced hepatocellular carcinoma: factors, strategies, and ongoing trials

Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer, representing approximately 85% of cases. The diagnosis is often made in the middle and late stages, necessitating systemic treatment as the primary therapeutic option. Despite sorafenib being the established standard of care for advanced HCC in the past decade, the efficacy of systemic therapy remains unsatisfactory, highlighting the need for novel treatment modalities. Recent breakthroughs in immunotherapy have shown promise in HCC treatment, particularly with immune checkpoint inhibitors (ICIs). However, the response rate to ICIs is currently limited to approximately 15%-20% of HCC patients. Recently, ICIs demonstrated greater efficacy in "hot" tumors, highlighting the urgency to devise more effective approaches to transform "cold" tumors into "hot" tumors, thereby enhancing the therapeutic potential of ICIs. This review presented an updated summary of the factors influencing the effectiveness of immunotherapy in HCC treatment, identified potential combination therapies that may improve patient response rates to ICIs, and offered an overview of ongoing clinical trials focusing on ICI-based combination therapy.

Hypoxia-based classification and prognostic signature for clinical management of hepatocellular carcinoma

OBJECTIVE

Intratumoral hypoxia is an essential feature of hepatocellular carcinoma (HCC). Herein, we investigated the hypoxia-based heterogeneity and relevant clinical implication in HCC.

METHODS

Three HCC cohorts: TCGA-LIHC, LICA-FR, and LIRI-JP were retrospectively gathered. Consensus clustering analysis was utilized for hypoxia-based classification based upon transcriptome of hypoxia genes. Through LASSO algorithm, a hypoxia-relevant prognostic signature was built. Immunotherapeutic response was inferred through analyzing immune checkpoints, T cell inflamed score, TIDE score, and TMB score. RNF145 expression was measured in normoxic or hypoxic HCC cells. In RNF145-knockout cells, CCK-8, TUNEL, and scratch tests were implemented.

RESULTS

HCC patients were classified into two hypoxia subtypes, with more advanced stages and poorer prognosis in cluster2 than cluster1. The heterogeneity in tumor infiltrating immune cells and genetic mutation was found between subtypes. The hypoxia-relevant prognostic model was proposed, composed of ANLN, CBX2, DLGAP5, FBLN2, FTCD, HMOX1, IGLV1-44, IL33, LCAT, LPCAT1, MKI67, PFN2, RNF145, S100A9, and SPP1). It was predicted that high-risk patients presented worse prognosis with an independent and reliable manner. Based upon high expression of immune checkpoints (CD209, CTLA4, HAVCR2, SIRPA, TNFRSF18, TNFRSF4, and TNFRSF9), high T cell inflamed score, low TIDE score and high TMB score, high-risk patients might respond to immunotherapy. Experimental validation showed that RNF145 was upregulated in hypoxic HCC cells, RNF145 knockdown attenuated proliferation and migration, but aggravated apoptosis in HCC cells.

CONCLUSION

Altogether, the hypoxia-based classification and prognostic signature might be useful for prognostication and guiding treatment of HCC.

Prognostic and immunological significance of an M1 macrophage-related gene signature in osteosarcoma

As the most abundant infiltrating immune cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs) are pivotal in tumor development and treatment. The present investigation endeavors to explore the potential of M1 macrophage-related genes (MRGs) as biomarkers for assessing risk in individuals with osteosarcoma. RNA-sequence data and clinical data were derived from TCGA and GEO databases. The CIBERSORT method was utilized to discern subtypes of tumor-infiltrating immune cells. Identification of MRGs was achieved through Pearson correlation analysis. A prognostic risk model for MRGs was developed using Cox and LASSO regression analyses. A tripartite gene signature comprising CD37, GABRD, and ARHGAP25 was an independent prognostic indicator and was employed to develop a risk score model. The internal and external validation cohort confirmed the results. The area under the ROC curve (AUC) was determined for survival periods of 1 year, three years, and five years, yielding values of 0.746, 0.839, and 0.850, respectively. The C-index of the risk score was found to be superior to clinicopathological factors. GO/KEGG enrichment showed that the differences between high- and low-risk groups were predominantly associated with immune response pathways. Immune-related analysis related to proportions of immune cells, immune function, and expression levels of immune checkpoint genes all showed differences between the high- and low-risk groups. The qRT-PCR and Western blotting results indicate that CD37 expression was markedly higher in MG63 and U2OS cell lines when compared to normal osteoblast hFOB1.19. In U2OS cell line, GABRD expression levels were significantly upregulated. ARHGAP25 expression levels were elevated in both 143B and U2OS cell lines. In summary, utilizing a macrophage genes signature demonstrates efficacy in predicting both the prognosis and therapy response of OS. Additionally, immune analysis confirms a correlation between the risk score and the tumor microenvironment. Our findings, therefore, provide a cogent account for the disparate prognoses observed among patients and furnish a justification for further inquiry into biomarkers and anti-tumor treatment strategies.

PANoptosis-based molecular subtyping and HPAN-index predicts therapeutic response and survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a highly prevalent and fatal cancer. The role of PANoptosis, a novel form of programmed cell death, in HCC is yet to be fully understood. This study focuses on identifying and analyzing PANoptosis-associated differentially expressed genes in HCC (HPAN_DEGs), aiming to enhance our understanding of HCC pathogenesis and potential treatment strategies.

Methods

We analyzed HCC differentially expressed genes from TCGA and IGCG databases and mapped them to the PANoptosis gene set, identifying 69 HPAN_DEGs. These genes underwent enrichment analyses, and consensus clustering analysis was used to determine three distinct HCC subgroups based on their expression profiles. The immune characteristics and mutation landscape of these subgroups were evaluated, and drug sensitivity was predicted using the HPAN-index and relevant databases.

Results

The HPAN_DEGs were mainly enriched in pathways associated with the cell cycle, DNA damage, Drug metabolism, Cytokines, and Immune receptors. We identified three HCC subtypes (Cluster_1, SFN+PDK4-; Cluster_2, SFN-PDK4+; Cluster_3, SFN/PDK4 intermediate expression) based on the expression profiles of the 69 HPAN_DEGs. These subtypes exhibited distinct clinical outcomes, immune characteristics, and mutation landscapes. The HPAN-index, generated by machine learning using the expression levels of 69 HPAN_DEGs, was identified as an independent prognostic factor for HCC. Moreover, the high HPAN-index group exhibited a high response to immunotherapy, while the low HPAN-index group showed sensitivity to small molecule targeted drugs. Notably, we observed that the YWHAB gene plays a significant role in Sorafenib resistance.

Conclusion

This study identified 69 HPAN_DEGs crucial to tumor growth, immune infiltration, and drug resistance in HCC. Additionally, we discovered three distinct HCC subtypes and constructed an HPAN-index to predict immunotherapeutic response and drug sensitivity. Our findings underscore the role of YWHAB in Sorafenib resistance, presenting valuable insights for personalized therapeutic strategy development in HCC.

DLAT as a Cuproptosis Promoter and a Molecular Target of Elesclomol in Hepatocellular Carcinoma

OBJECTIVE

Cuproptosis is a novel cell death pathway that was newly discovered in early 2022. However, cuproptosis is still in its infancy in many respects and warrants further research in hepatocellular carcinoma (HCC). This study aimed to analyze the mechanism of cuprptosis in HCC.

METHODS

Herein, the tumor microenvironment infiltration landscape of molecular subtypes was illustrated using GSVA, ssGSEA, TIMER, CIBERSORT, and ESTIMATE algorithms based on the expression profile of cuproptosis-related genes (CRGs) from TCGA and GEO databases. Then, the least absolute shrinkage and selection operator regression method was applied to construct a cuproptosis signature to quantify the cuproptosis profile of HCC. Further, we explored the expression of three hub CRGs in cell lines and clinical patient tissues of HCC by Western blotting, qRT-PCR and immunohistochemistry. Finally, we examined the function of dihydrolipoamide S-acetyltransferase (DLAT) in cuproptosis in HCC by loss-of-function strategy, Western blotting and CCK8 assay.

RESULTS

Three distinct molecular subtypes were identified. Cluster 2 had the greatest infiltration of immune cells with best prognosis. The cuproptosis signature was indicative of tumor subtype, immunity, and prognosis for HCC, and specifically, a low cuproptosis score foreshadowed good prognosis. DLAT was highly expressed in liver cancer cell lines and HCC tissues and positively correlated with clinical stage and grade. We also found that potent copper ionophore elesclomol could induce cuproptosis in a copper-dependent manner. Selective Cu⁺⁺ chelator ammonium tetrathiomolybdate and downregulating DLAT expression by siRNA could effectively inhibit cuproptosis.

CONCLUSION

Cuproptosis and DLAT as a promising biomarker could help to determine the prognosis of HCC and may offer novel insights for effective treatment.

Identification of biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in alcoholic hepatitis by bioinformatics and experimental verification

Backgrounds

Alcoholic hepatitis (AH) is a major health problem worldwide. There is increasing evidence that immune cells, iron metabolism and copper metabolism play important roles in the development of AH. We aimed to explore biomarkers that are co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.

Methods

GSE28619 and GSE103580 datasets were integrated, CIBERSORT algorithm was used to analyze the infiltration of 22 types of immune cells and GSVA algorithm was used to calculate ferroptosis and cuproptosis scores. Using the "WGCNA" R package, we established a gene co-expression network and analyzed the correlation between M1 macrophages, ferroptosis and cuproptosis scores and module characteristic genes. Subsequently, candidate genes were screened by WGCNA and differential expression gene analysis. The LASSO-SVM analysis was used to identify biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis. Finally, we validated these potential biomarkers using GEO datasets (GSE155907, GSE142530 and GSE97234) and a mouse model of AH.

Results

The infiltration level of M1 macrophages was significantly increased in AH patients. Ferroptosis and cuproptosis scores were also increased in AH patients. In addition, M1 macrophages, ferroptosis and cuproptosis were positively correlated with each other. Combining bioinformatics analysis with a mouse model of AH, we found that ALDOA, COL3A1, LUM, THBS2 and TIMP1 may be potential biomarkers co-associated with M1 macrophages, ferroptosis and cuproptosis in AH patients.

Conclusion

We identified 5 potential biomarkers that are promising new targets for the treatment and diagnosis of AH patients.

Identifying hub circadian rhythm biomarkers and immune cell infiltration in rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease with symptoms characterized by typical circadian rhythmic changes. This study aimed to identify the hub circadian rhythm genes (CRGs) in RA and explore their association with immune cell infiltration and pathogenesis of RA.

Methods

The differentially expressed CRGs (DECRGs) between RA and normal control samples were screened from Datasets GSE12021 and GSE55235. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to explore the potential functional mechanisms of DECRGs in RA. Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator regression analysis were performed to identify hub CRGs of RA. CIBERSORT was conducted to compare the infiltration level of immune cells in RA and control synovial tissue and their relationship with hub genes. In addition, the diagnostic value of hub biomarkers was evaluated by the area under the receiver operator characteristic curve. Further, a nomogram prediction model was constructed and its significance for clinical decision-making was evaluated.

Results

The green module was identified as the hub module associated with RA. Four hub CRGs (EGR1, FOSL2, GADD45B, and NFIL3) were identified and showed that they had the highest specificity and sensitivity for RA diagnosis, respectively. The expression levels and diagnostic values of these genes were externally validated in the dataset GSE55457. A nomogram prediction model based on the four hub CRGs was constructed and proved to have a certain clinical decision value. Additionally, the correlation analysis of immune cells with hub genes showed that all hub genes were significantly positively correlated with activated mast cells, resting memory CD4+ T cells, and monocytes. Whereas, all hub genes were negatively correlated with plasma cells, CD8+ T cells, and activated memory CD4+ T cells. Meanwhile, FOSL2 and GADD45B were negatively correlated with Tfh cells.

Conclusion

Four hub CRGs were identified and showed excellent diagnostic value for RA. These genes may be involved in the pathological process of RA by disrupting the rhythmic oscillations of cytokines through immune-related pathways and could be considered molecular targets for future chronotherapy against RA.

Development of a novel hypoxia-immune–related LncRNA risk signature for predicting the prognosis and immunotherapy response of colorectal cancer

Background

Colorectal cancer (CRC) is one of the most common digestive system tumors worldwide. Hypoxia and immunity are closely related in CRC; however, the role of hypoxia-immune–related lncRNAs in CRC prognosis is unknown.

Methods

Data used in the current study were sourced from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA) databases. CRC patients were divided into low- and high-hypoxia groups using the single-sample gene set enrichment analysis (ssGSEA) algorithm and into low- and high-immune groups using the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm. Differentially expressed lncRNAs (DElncRNAs) between low- and high-hypoxia groups, low- and high-immune groups, and tumor and control samples were identified using the limma package. Hypoxia-immune–related lncRNAs were obtained by intersecting these DElncRNAs. A hypoxia-immune–related lncRNA risk signature was developed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses. The tumor microenvironments in the low- and high-risk groups were evaluated using ssGSEA, ESTIMATE, and the expression of immune checkpoints. The therapeutic response in the two groups was assessed using TIDE, IPS, and IC50. A ceRNA network based on signature lncRNAs was constructed. Finally, we used RT-qPCR to verify the expression of hypoxia-immune–related lncRNA signatures in normal and cancer tissues.

Results

Using differential expression analysis, and univariate Cox and LASSO regression analyses, ZNF667-AS1, LINC01354, LINC00996, DANCR, CECR7, and LINC01116 were selected to construct a hypoxia-immune–related lncRNA signature. The performance of the risk signature in predicting CRC prognosis was validated in internal and external datasets, as evidenced by receiver operating characteristic curves. In addition, we observed significant differences in the tumor microenvironment and immunotherapy response between low- and high-risk groups and constructed a CECR7–miRNA–mRNA regulatory network in CRC. Furthermore, RT-qPCR results confirmed that the expression patterns of the six lncRNA signatures were consistent with those in TCGA-CRC cohort.

Conclusion

Our study identified six hypoxia-immune–related lncRNAs for predicting CRC survival and sensitivity to immunotherapy. These findings may enrich our understanding of CRC and help improve CRC treatment. However, large-scale long-term follow-up studies are required for verification.

PTPRO-related CD8+ T-cell signatures predict prognosis and immunotherapy response in patients with breast cancer

BACKGROUND

Poor immunogenicity and extensive immunosuppressive T-cell infiltration in the tumor immune microenvironment (TIME) have been identified as potential barriers to immunotherapy success in "immune-cold" breast cancers. Thus, it is crucial to identify biomarkers that can predict immunotherapy efficacy. Protein tyrosine phosphatase receptor type O (PTPRO) regulates multiple kinases and pathways and has been implied to play a regulatory role in immune cell infiltration in various cancers.

METHODS

ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) were performed to uncover the TIME landscape. The correlation analysis of PTPRO and immune infiltration was performed to characterize the immune features of PTPRO. Univariate and multivariate Cox analyses were applied to determine the prognostic value of various variables and construct the PTPRO-related CD8⁺ T-cell signatures (PTSs). The Kaplan-Meier curve and the receiver operating characteristic (ROC) curve were used to estimate the performance of PTS in assessing prognosis and immunotherapy response in multiple validation datasets.

RESULTS

High PTPRO expression was related to high infiltration levels of CD8⁺ T cells, as well as macrophages, activated dendritic cells (aDCs), tumor-infiltrating lymphocytes (TILs), and Th1 cells. Given the critical role of CD8⁺ T cells in the TIME, we focused on the impact of PTPRO expression on CD8⁺ T-cell infiltration. The prognostic PTS was then constructed using the TCGA training dataset. Further analysis showed that the PTS exhibited favorable prognostic performance in multiple validation datasets. Of note, the PTS could accurately predict the response to immune checkpoint inhibitors (ICIs).

CONCLUSION

PTPRO significantly impacts CD8⁺ T-cell infiltration in breast cancer, suggesting a potential role of immunomodulation. PTPRO-based PTS provides a new immune cell paradigm for prognosis, which is valuable for immunotherapy decisions in cancer patients.

Development and validation of a cancer-associated fibroblast-derived lncRNA signature for predicting clinical outcomes in colorectal cancer

Cancer-associated fibroblasts (CAFs) are actively involved in cancer progression through generating extracellular matrix and orchestrating the crosstalk within the tumor microenvironment (TME). This study aimed to develop and validate a CAF-derived lncRNA (long non-coding RNA) (CAFDL) signature for predicting clinical outcomes in colorectal cancer (CRC). Clinical data and transcriptomic profiles of 2,320 patients with CRC from The Cancer Genome Atlas (TCGA)-COAD and TCGA-READ datasets and 16 Gene Expression Omnibus datasets were included in this study. CAFDLs were identified using weighted gene co-expression network analysis. The CAFDL signature was constructed using the least absolute shrinkage and selection operator analysis in the TCGA-CRC training set. Multiple CRC cohorts and pan-cancer cohorts were used to validated the CAFDL signature. Patients with high CAFDL scores had significantly worse overall survival and disease-free survival than patients with low CAFDL scores in all CRC cohorts. In addition, non-responders to fluorouracil, leucovorin, and oxaliplatin (FOLFOX)/fluorouracil, leucovorin, and irinotecan (FOLFIRI) chemotherapy, chemoradiotherapy, bevacizumab, and immune checkpoint inhibitors had significantly higher CAFDL scores compared with responders. Pan-cancer analysis showed that CAFDL had prognostic predictive power in multiple cancers such as lung adenocarcinoma, breast invasive carcinoma, stomach adenocarcinoma, and thyroid carcinoma. The CAFDL signature was positively correlated with transforming growth factor-beta (TGF-β) signaling, epithelial–mesenchymal transition, and angiogenesis pathways but negatively correlated with the expression of immune checkpoints such as PDCD1, CD274, and CTLA4. The CAFDL signature reflects CAF properties from a lncRNA perspective and effectively predicts clinical outcomes in CRC and across pan-cancer. The CAFDL signature can serve as a useful tool for risk stratification and provide new insights into the underlying mechanisms of CAFs in cancer immunity.