Weighted Gene Co-expression Network Analysis Identifies a Cancer-Associated Fibroblast Signature for Predicting Prognosis and Therapeutic Responses in Gastric Cancer

Identification of a novel cancer-associated fibroblasts gene signature based on bioinformatics analysis to predict prognosis and therapeutic responses in breast cancer

Cancer-associated fibroblasts (CAFs) provide suitable conditions for growth of tumor cell and facilitate tumor progression. Hence, we aimed to identify a CAFs-related gene signature associated with the prognosis of patients with breast cancer (BRCA). We downloaded datasets from Gene Expression Omnibus (GEO) and confirmed the correlation between CAFs infiltration scores and prognosis. By performing weighted gene co-expression network analysis (WGCNA) and Lasso Cox regression analysis, we constructed a four-gene (COL5A3, FN1, POSTN, and RARRES2) prognostic CAFs signature model. Based on the median risk score of CAFs, patients with BRCA were divided into high- and low-risk groups. Compared with low-risk group, patients in high-risk group exhibited a poor prognosis and limited response to immunotherapy. Furthermore, patients with high CAFs risk scores were found to have a detrimental prognosis due to the induction of immunosuppressive cell infiltration, resulting in an immunosuppressive tumor microenvironment. Importantly, we found that CAFs overexpressing FN1 and POSTN significantly promoted the wound healing and invasion ability of tumor cells in vitro validation. Taking together, we identified a four-gene prognostic CAFs signature, which was proven to be a reliable indicator for prognosis and therapeutic efficacy in patients with BRCA. This study provided evidence for novel CAFs-based stromal therapy.

Identification of hub genes and diagnostic efficacy for triple-negative breast cancer through WGCNA and Mendelian randomization

OBJECTIVE

Triple-negative breast cancer (TNBC) represents a particularly aggressive form of breast cancer with a poor prognosis due to a lack of targeted treatments resulting from limited a understanding of the underlying mechanisms. The aim of this study was the identification of hub genes for TNBC and assess their clinical applicability in predicting the disease.

METHODS

This study employed a combination of weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) to identify new susceptible modules and central genes in TNBC. The potential functional roles of the central genes were investigated using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Furthermore, a predictive model and ROC curve were developed to assess the diagnostic performance of the identified central genes. The correlation between CCNB1 and immune cells proportion was also investigated. At last, a Mendelian randomization (MR) analysis utilizing Genome-Wide Association Study (GWAS) data was analyzed to establish the causal effect of CCNB1 level on TNBC.

RESULTS

WGCNA was applied to determine gene co-expression maps and identify the most relevant module. Through a screening process, 1585 candidate hub genes were subsequently identified with WGCNA and DEGs. GO and KEGG function enrichment analysis indicated that these core genes were related to various biological processes, such as organelle fission, chromosome segregation, nuclear division, mitotic cell cycle phase transition, the cell cycle, amyotrophic lateral sclerosis, and motor proteins. Using STRING and Cytoscape, the top five genes with high degrees were identified as CDC2, CCNB1, CCNA2, TOP2A, and CCNB2. The nomogram model demonstrated good performance in predicting TNBC risk and was proven effective in diagnosis, as evidenced by the receiver operating characteristic (ROC) curve. Further investigation revealed a causal association between CCNB1 and immune cell infiltrates in TNBC. Survival analysis revealed high expression of the CCNB1 gene leads to poorer prognosis in TNBC patients. Additionally, analysis using inverse variance weighting revealed that CCNB1 was linked to a 2.8% higher risk of TNBC (OR: 1.028, 95% CI 1.002-1.055, p = 0.032).

CONCLUSION

We established a co-expression network using the WGCNA methodology to detect pivotal genes associated with TNBC. This finding holds promise for advancing the creation of pre-symptomatic diagnostic tools and deepening our comprehension of the pathogenic mechanisms involved in TNBC risk genes.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Knockdown of ADAM8 inhibits the proliferation, migration, invasion, and tumorigenesis of renal clear cell carcinoma cells to enhance the immunotherapy efficacy

The current study performed bioinformatics and in vitro and in vivo experiments to explore the effects of ADAM8 on the malignant behaviors and immunotherapeutic efficacy of renal clear cell carcinoma (ccRCC) Cells. The modular genes most associated with immune cells were screened. Then, prognostic risk models were constructed by univariate COX analysis, LASSO regression analysis and multivariate COX analysis, and their diagnostic value was determined. The correlation between tumor mutation load (TMB) scores and the prognosis of ccRCC patients was clarified. Finally, six key genes (ABI3, ADAM8, APOL3, MX2, CCDC69, and STAC3) were analyzed for immunotherapy efficacy. Human and mouse ccRCC cell lines and human proximal tubular epithelial cell lines were used for in vitro cell experiments. The effect of ADAM8 overexpression or knockdown on tumor formation and survival in ccRCC cells was examined by constructing subcutaneous transplanted tumor model. Totally, 636 Black module genes were screened as being most associated with immune cell infiltration. Six genes were subsequently confirmed for the construction of prognostic risk models, of which ABI3, APOL3 and CCDC69 were low-risk factors, while ADAM8, MX2 and STAC3 were high-risk factors. The constructed risk model based on the identified six genes could accurately predict the prognosis of ccRCC patients. Besides, TMB was significantly associated with the prognosis of ccRCC patients. Furthermore, ABI3, ADAM8, APOL3, MX2, CCDC69 and STAC3 might play important roles in treatment concerning CTLA4 inhibitors or PD-1 inhibitors or combined inhibitors. Finally, we confirmed that ADAM8 could promote the proliferation, migration and invasion of ccRCC cells through in vitro experiments, and further found that in in vivo experiments, ADAM8 knockdown could inhibit tumor formation in ccRCC cells, improve the therapeutic effect of anti-PD1, and prolong the survival of mice. Our study highlighted the alleviative role of silencing ADAM8 in ccRCC patients.

Toward a prognostic model for all-cause mortality among old people with disability in long-term care in China

BACKGROUND

Current prognostic model of all-cause mortality may not be applicable for old people with disability in long-term care due to the absence of injury- and care-related predictors. We aimed to develop a prognostic model specifically tailored to this population, based on comprehensive predictors.

METHOD

We conducted a prospective study involving 41,004 participants aged ≥60 with disability in long-term care across 16 study sites in Southwest China from 2017 to 2021. Participants' demographics, clinical characteristics, disability status, and injury- and care-related information at baseline were used as candidate predictors. We employed a LASSO Cox regression model to develop the prognostic model using the training set (70 % of participants), and the predictive performance was validated in the validation set (30 % of participants). The prognostic index (PI) scores of the prognostic model were used to quantify mortality risk.

RESULTS

At the end of the 4-year follow-up, 17,797 deaths (43.4 %) were observed. The prognostic model revealed several powerful and robust predictors of mortality across the total sample and subgroups, including higher age, living with comorbidities, physical and perceptual disability, and living with pressure sores. Non-professional care was an additional predictor in older participants. The risk of death for participants in the highest quartile of PI scores was approximately four-fold higher compared to those in the lowest quartile.

CONCLUSIONS

We developed and validated a prognostic model that can be practically utilized to identify individuals and populations at risk of death among old people with disability in long-term care.

Machine Learning-based Development and Validation of a Cell Senescence Predictive and Prognostic Signature in Intrahepatic Cholangiocarcinoma

Background: Previous studies have shown that cellular senescence is strongly associated with tumorigenesis and the tumor microenvironment. Accordingly, we developed a novel prognostic signature for intrahepatic cholangiocarcinoma (ICCA) based on senescence-associated long non-coding RNAs (SR-lncRNAs) and identified a lncRNA-miRNA-mRNA axis involving in ICCA. Methods: Based on the 197 senescence-associated genes (SRGs) from Genacards and their expression in Fu-ICCA cohort, we identified 20 lncRNAs as senescence-associated lncRNAs (SR-lncRNAs) through co-expression and cox-regression analysis. According to 20 SR-lncRNAs, patients with ICCA were classified into 2 molecular subtypes using unsupervised clustering machine learning approach and to explore the prognostic and functional heterogeneity between these two subtypes. Subsequently, we integrated 113 machine learning algorithms to develop senescence-related lncRNA signature, ultimately identifying 11 lncRNAs and constructing prognostic models and risk stratification. The correlation between the signature and the immune landscape, immunotherapy response as well as drug sensitivity are explored too. Results: We developed a novel senescence related signature. The predictive model and risk score calculated by the signature exhibited favorable prognostic predictive performance, which is a suitable independent risk factor for the prognosis of patients with ICCA based on Kaplan-Meier plotter, nomogram and receiving operating characteristic (ROC) curves. The results were validated using external datasets. Estimate, ssGSEA (single sample gene set enrichment analysis), IPS (immunophenotype score) and TIDE (tumor immune dysfunction and exclusion) algorithms revealed higher immune infiltration, higher immune scores, lower immune escape potential and better response to immunotherapy in the high-risk group. In addition, signature identifies eight chemotherapeutic agents, including cisplatin for patients with different risk levels, providing guidance for clinical treatment. Finally, we identified a set of lncRNA-miRNA-mRNA axes involved in ICCA through regulation of senescence. Conclusion: SR-lncRNAs signature can favorably predict the prognosis, risk stratification, immune landscape and immunotherapy response of patients with ICCA and consequently guide individualized treatment.

Exercise potentially prevents colorectal cancer liver metastases by suppressing tumor epithelial cell stemness via RPS4X downregulation

Background

Colorectal cancer (CRC) is the third most prevalent tumor globally. The liver is the most common site for CRC metastasis, and the involvement of the liver is a common cause of death in patients with late-stage CRC. Consequently, mitigating CRC liver metastasis (CRLM) is key to improving CRC prognosis and increasing survival. Exercise has been shown to be an effective method of improving the prognosis of many tumor types. However, the ability of exercise to inhibit CRLM is yet to be thoroughly investigated.

Methods

The GSE157600 and GSE97084 datasets were used for analysis. A pan-cancer dataset which was uniformly normalized was downloaded and analyzed from the UCSC database: TCGA, TARGET, GTEx (PANCAN, n = 19,131, G = 60,499). Several advanced bioinformatics analyses were conducted, including single-cell sequencing analysis, correlation algorithm, and prognostic screen. CRC tumor microarray (TMA) as well as cell/animal experiments are used to further validate the results of the analysis.

Results

The greatest variability was found in epithelial cells from the tumor group. RPS4X was generally upregulated in all types of CRC, while exercise downregulated RPS4X expression. A lowered expression of RPS4X may prolong tumor survival and reduce CRC metastasis. RPS4X and tumor stemness marker-CD44 were highly positively correlated and knockdown of RPS4X expression reduced tumor stemness both in vitro and in vivo.

Conclusion

RPS4X upregulation may enhance CRC stemness and increase the odds of metastasis. Exercise may reduce CRC metastasis through the regulation of RPS4X.

Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer

BACKGROUND

Breast cancer (BC), a leading malignant disease, affects women all over the world. Cancer associated fibroblasts (CAFs) stimulate epithelial-mesenchymal transition, and induce chemoresistance and immunosuppression.

AIM

To establish a CAFs-associated prognostic signature to improve BC patient outcome estimation.

METHODS

We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas (TCGA) databases, and 3661 BC samples from the The Gene Expression Omnibus. CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm. CAF-associated gene identification was done by weighted gene co-expression network analysis. A CAF risk signature was established via univariate, least absolute shrinkage and selection operator regression, and multivariate Cox regression analyses. The receiver operating characteristic (ROC) and Kaplan-Meier curves were employed to evaluate the predictability of the model. Subsequently, a nomogram was developed with the risk score and patient clinical signature. Using Spearman's correlations analysis, the relationship between CAF risk score and gene set enrichment scores were examined. Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

Employing an 8-gene (IL18, MYD88, GLIPR1, TNN, BHLHE41, DNAJB5, FKBP14, and XG) signature, we attempted to estimate BC patient prognosis. Based on our analysis, high-risk patients exhibited worse outcomes than low-risk patients. Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis. ROC analysis exhibited satisfactory nomogram predictability. The area under the curve showed 0.805 at 3 years, and 0.801 at 5 years in the TCGA cohort. We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes. CAF risk score was significantly correlated with most hallmark gene sets. Finally, the prognostic signature were further validated by qRT-PCR.

CONCLUSION

We introduced a newly-discovered CAFs-associated gene signature, which can be employed to estimate BC patient outcomes conveniently and accurately.

Prediction of CAF-related genes in immunotherapy and drug sensitivity in hepatocellular carcinoma: a multi-database analysis

This study aims to identify the cancer-associated fibroblasts (CAF)-related genes that can affect immunotherapy and drug sensitivity in hepatocellular carcinoma (HCC). Expression data and survival data associated with HCC were obtained in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted correlation network analysis (WGCNA) analysis was performed to obtain CAF-related genes. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for regression analysis and risk models. Subsequently, Gene Set Enrichment Analysis (GSEA) analysis, Gene Set Enrichment Analysis (ssGSEA) analysis, Tumor Immune Dysfunction and Exclusion (TIDE) analysis and drug sensitivity analysis were performed on the risk models. Survival analysis of CAF scores showed that the survival rate was lower in samples with high CAF scores than those with low scores. However, this difference was not significant, suggesting CAF may not directly influence the prognosis of HCC patients. Further screening of CAF-related genes yielded 33 CAF-related genes. Seven risk models constructed based on CDR2L, SPRED1, PFKP, ENG, KLF2, FSCN1 and VCAN, showed significant differences in immunotherapy and partial drug sensitivity in HCC. Seven CAF-related genes may have important roles in immunotherapy, drug sensitivity and prognostic survival in HCC patients.

The collagen landscape in cancer: profiling collagens in tumors and in circulation reveals novel markers of cancer-associated fibroblast subtypes

Cancer-associated fibroblasts (CAFs) deposit and remodel collagens in the tumor stroma, impacting cancer progression and efficacy of interventions. CAFs are the focus of new therapeutics with the aim of normalizing the tumor microenvironment. To do this, a better understanding of CAF heterogeneity and collagen composition in cancer is needed. In this study, we sought to profile the expression of collagens at multiple levels with the goal of identifying cancer biomarkers. We investigated the collagen expression pattern in various cell types and CAF subtypes in a publicly available single-cell RNA sequencing (RNA-seq) dataset of pancreatic ductal adenocarcinoma. Next, we investigated the collagen expression profile in tumor samples across cancer types from The Cancer Genome Atlas (TCGA) database and evaluated if specific patterns of collagen expression were associated with prognosis. Finally, we profiled circulating collagen peptides using a panel of immunoassays to measure collagen fragments in the serum of cancer patients. We found that pancreatic stellate cells and fibroblasts were the primary producers of collagens in the pancreas. COL1A1, COL3A1, COL5A1, COL6A1 were expressed in all CAF subtypes, whereas COL8A1, COL10A1, COL11A1, COL12A1 were specific to myofibroblast CAFs (myCAF) and COL14A1 specific to inflammatory CAFs (iCAF). In TCGA database, myCAF collagens COL10A1 and COL11A1 were elevated across solid tumor types, and multiple associations between high expression and worse survival were found. Finally, circulating collagen biomarkers were elevated in the serum of patients with cancer relative to healthy controls with COL11A1 (myCAF) having the best diagnostic accuracy of the markers measured. In conclusion, CAFs express a noncanonical collagen profile with specific collagen subtypes associated with iCAFs and myCAFs in PDAC. These collagens are deregulated at the cellular, tumor, and systemic levels across different solid tumors and associate with survival. These findings could lead to new discoveries such as novel biomarkers and therapeutic targets. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Molecular Mechanism of Circ_0088300-BOLL Interaction Regulating Mitochondrial Metabolic Reprogramming and Involved in Gastric Cancer Growth and Metastasis

This study aims to investigate the effect and molecular mechanism of the interaction between circRNA circ_0088300 and the RNA binding protein (RBP) BOLL on the growth and metastasis of gastric cancer. A prognostic risk model was established by screening differentially expressed RBP genes from the TCGA database, and BOLL was identified as a critical RBP. Gene Set Enrichment analysis (GSEA) showed that BOLL was associated with mitochondrial function. The upregulation fold change of circ_0088300 was the highest in the GSE93541 data set, and the RPISeq database confirmed its binding relationship with BOLL. In vitro experiments showed that BOLL regulates mitochondrial metabolism and cancer cell function and circ_0088300 upregulates the expression level of BOLL. In vivo experiments demonstrated that knocking down circ_0088300 can inhibit tumor growth and metastasis, whereas overexpression of BOLL can reverse this effect. In conclusion, we have reached a preliminary conclusion that upregulation of BOLL by circ_0088300 promotes gastric cancer growth and metastasis by promoting mitochondrial metabolic reprogramming.

Microdissection of cancer-associated fibroblast infiltration subtypes unveils the secreted SERPINE2 contributing to immunosuppressive microenvironment and immuotherapeutic resistance in gastric cancer: A large-scale study integrating bulk and single-cell transcriptome profiling

In the era of immunotherapy, the suboptimal response rate and the development of acquired resistance among the initial beneficiaries continue to present significant challenges across multiple malignancies, including gastric cancer (GC). Considering that the interactions of tumor stroma, especially the cancer-associated fibroblasts (CAFs), with immune and tumor cells, play indispensable roles in tumor progression, tumor microenvironment remodeling and therapeutic responsiveness, in-depth exploration on the roles of CAFs and pivotal mediators of their functions may provide novel clues to increase the effectiveness of current immunotherapeutic drugs and further achieve synergistic antitumor response. Herein, through the consensus clustering of canonical biomarkers, three GC subclasses with different abundance of CAFs were virtually microdissected in four integrated bulk cohorts encompassing 2148 GC patients from 11 independent datasets. An extensive immunogenomic analysis revealed that tumors with high CAFs infiltration were characterized with unfavorable outcomes, aggressive phenotypes, decreased tumor immunogenicity, high risk of immune evasion and thus immunotherapeutic resistance. By leveraging large-scale single-cell transcriptomic profiling, a series of CAF-secreted proteins were identified, among which the SERPINE2 was confirmed to be restrictively enriched in stromal fibroblasts of GC tissues and contribute to promoting a protumor milieu and fostering an immunosuppressive microenvironment via bioinformatics computations and tissue microarray analysis. Moreover, pan-cancer investigations generalized the immunological roles of SERPINE2, especially in pan-gastrointestinal malignancies, with multiple real-world immunotherapy cohorts further confirming its implications on predicting immunotherapeutic efficacy. In conclusion, these findings suggest that the CAF-derived SERPINE2 is a promising immune-oncology target with therapeutic implications to further synergize the immunotherapeutic combinations.

Dual effects of radiotherapy on tumor microenvironment and its contribution towards the development of resistance to immunotherapy in gastrointestinal and thoracic cancers

Successful clinical methods for tumor elimination include a combination of surgical resection, radiotherapy, and chemotherapy. Radiotherapy is one of the crucial components of the cancer treatment regimens which allow to extend patient life expectancy. Current cutting-edge radiotherapy research is focused on the identification of methods that should increase cancer cell sensitivity to radiation and activate anti-cancer immunity mechanisms. Radiation treatment activates various cells of the tumor microenvironment (TME) and impacts tumor growth, angiogenesis, and anti-cancer immunity. Radiotherapy was shown to regulate signaling and anti-cancer functions of various TME immune and vasculature cell components, including tumor-associated macrophages, dendritic cells, endothelial cells, cancer-associated fibroblasts (CAFs), natural killers, and other T cell subsets. Dual effects of radiation, including metastasis-promoting effects and activation of oxidative stress, have been detected, suggesting that radiotherapy triggers heterogeneous targets. In this review, we critically discuss the activation of TME and angiogenesis during radiotherapy which is used to strengthen the effects of novel immunotherapy. Intracellular, genetic, and epigenetic mechanisms of signaling and clinical manipulations of immune responses and oxidative stress by radiotherapy are accented. Current findings indicate that radiotherapy should be considered as a supporting instrument for immunotherapy to limit the cancer-promoting effects of TME. To increase cancer-free survival rates, it is recommended to combine personalized radiation therapy methods with TME-targeting drugs, including immune checkpoint inhibitors.

A WGCNA-based cancer-associated fibroblast risk signature in colorectal cancer for prognosis and immunotherapy response

Background

Cancer-associated fibroblasts (CAFs) are notably involved in colorectal cancer (CRC) tumorigenesis, progression, and treatment failure. In this article, we report the in silico development of a CAF-related prognostic signature for CRC.

Methods

We separately downloaded CRC transcription data from The Cancer Genome Atlas and the Gene Expression Omnibus database. Deconvolution algorithms, including Estimating the Proportions of Immune and Cancer Cells and the Microenvironment Cell Population-counter, were used to calculate CAF abundance, while the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression algorithm was used to calculate the stromal score. Weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator algorithm were used to identify CAF-related genes and prognostic signatures.

Results

We identified a three-gene, prognostic, CAF-related signature and defined risk groups based on the Riskscores. Multidimensional validations were applied to evaluate the robustness of the signature and its correlation with clinical parameters. We utilized Tumor Immune Dysfunction and Exclusion (TIDE) and oncoPredict algorithms to predict therapy responses and found that patients in low-risk groups are more sensitive to immunotherapy and chemotherapy drugs such as 5-fluorouracil and oxaliplatin. Finally, we used the Cancer Cell Line Encyclopedia and Human Protein Atlas databases to evaluate the mRNA and protein levels encoded by the signature genes.

Conclusions

This novel CAF-related three-gene signature is expected to become a potential prognostic biomarker in CRC and predict chemotherapy and immunotherapy responses. It may be of considerable value for studying the tumor microenvironment in CRC.

Predicting Survival Signature of Bladder Cancer Related to Cancer-Associated Fibroblast (CAF) Constructed by Intersecting Genes in TCGA and GEO

Bladder cancer was one of the most common carcinomas around the world. However, the mechanism of the disease still remained to be investigated. We expected to establish a prognostic survival model with 9 prognostic genes to predict overall survival (OS) in patients of bladder cancer. The gene expression data of bladder cancer were obtained from TCGA and GEO datasets. TCGA and GEO datasets were used for screening prognostic genes along with developing and validating a 9-gene prognostic survival model by method of weighted gene co-expression network analysis (WGCNA) and LASSO with Cox regression. The relative analysis of evaluate tumor burden mutation (TBM), GO, KEGG, chemotherapy drug and functional pathway were also performed based on CAF-related mRNAs. 151 Overlapping CAF-related genes were distinguished after intersecting differentially expressed genes from 945 genes in TCGA and 491 genes in GEO dataset. 9 Prognostic genes (MSRB2, AGMAT, KLF6, DDAH2, GADD45B, SERPINE2, STMN3, TEAD2, and COMP) were used for construction of prognostic model after LASSO with Cox regression. Receiver operating characteristic (ROC) curves showed a good survival prediction by this model. Functional analysis indicated chemokine, cytokine, ECM interaction, oxidative stress and apoptosis were highly appeared. Potential drugs targeted different CAF-related genes like vemurafenib, irofulven, ghiotepa, and idarubicin were found as well. We constructed a novel 9 CAF-related mRNAs prognostic model and explored the gene expression and potential functional information of related genes, which might be worthy of clinical application. In addition, potential chemotherapy drugs could provide useful insights into the potential clinical treatment of bladder cancer.

Identification of a cancer-associated fibroblast classifier for predicting prognosis and therapeutic response in lung squamous cell carcinoma

Reliable prognostic gene signatures for cancer-associated fibroblasts (CAFs) in lung squamous cell carcinoma (LUSC) are still lacking, and the underlying genetic principles remain unclear. Therefore, the 2 main aims of our study were to establish a reliable CAFs prognostic gene signature that can be used to stratify patients with LUSC and to identify promising potential targets for more effective and individualized therapies. Clinical information and mRNA expression were accessed of the cancer genome atlas-LUSC cohort (n = 501) and GSE157011 cohort (n = 484). CAFs abundance were quantified by the multi-estimated algorithms. Stromal CAF-related genes were identified by weighted gene co-expression network analysis. The least absolute shrinkage and selection operator Cox regression method was utilized to identify the most relevant CAFs candidates for predicting prognosis. Chemotherapy sensitivity scores were calculated using the "pRRophetic" package in R software, and the tumor immune dysfunction and exclusion algorithm was employed to evaluate immunotherapy response. Gene set enrichment analysis and the Search Tool for Interaction of Chemicals database were applied to clarify the molecular mechanisms. In this study, we identified 288 hub CAF-related candidate genes by weighted gene co-expression network analysis. Next, 34 potential prognostic CAFs candidate genes were identified by univariate Cox regression in the cancer genome atlas-LUSC cohort. We prioritized the top 8 CAFs prognostic genes (DCBLD1, SLC24A3, ILK, SMAD7, SERPINE1, SNX9, PDGFA, and KLF10) by a least absolute shrinkage and selection operator Cox regression model, and these genes were used to identify low- and high-risk subgroups for unfavorable survival. In silico drug screening identified 6 effective compounds for high-risk CAFs-related LUSC: TAK-715, GW 441756, OSU-03012, MP470, FH535, and KIN001-266. Additionally, search tool for interaction of chemicals database highlighted PI3K-Akt signaling as a potential target pathway for high-risk CAFs-related LUSC. Overall, our findings provide a molecular classifier for high-risk CAFs-related LUSC and suggest that treatment with PI3K-Akt signaling inhibitors could benefit these patients.

Construction of a novel cancer-associated fibroblast-related signature to predict clinical outcome and immune response in colon adenocarcinoma

The interaction between the tumour and the surrounding microenvironment determines the malignant biological behaviour of the tumour. Cancer-associated fibroblasts (CAFs) coordinate crosstalk between cancer cells in the tumour immune microenvironment (TIME) and are extensively involved in tumour malignant behaviours, such as immune evasion, invasion and drug resistance. Here, we performed differential and prognostic analyses of genes associated with CAFs and constructed CAF-related signatures (CAFRs) to predict clinical outcomes in individuals with colon adenocarcinoma (COAD) based on machine learning algorithms. The CAFRs were further validated in an external independent cohort, GSE17538. Additionally, Cox regression, receiver operating characteristic (ROC) and clinical correlation analysis were utilised to systematically assess the CAFRs. Moreover, CIBERSORT, single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) analysis were utilised to characterise the TIME in patients with COAD. Microsatellite instability (MSI) and tumour mutation burden were also analysed. Furthermore, Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) elucidated the biological functions and signalling pathways involved in the CAFRs. Consensus clustering analysis was used for the immunological analysis of patients with COAD. Finally, the pRRophic algorithm was used for sensitivity analysis of common drugs. The CAFRs constructed herein can better predict the prognosis in COAD. The cluster analysis based on the CAFRs can effectively differentiate between immune 'hot' and 'cold' tumours, determine the beneficiaries of immune checkpoint inhibitors (ICIs) and provide insight into individualised treatment for COAD.

Herpesvirus latent infection promotes stroke via activating the OTUD1/NF-κB signaling pathway

OBJECTIVE

Our study aimed to reveal the molecular mechanisms underlying the regulation of cerebral infarction by herpes virus latency infection via the OTUD1/NF-κB signaling pathway using evidence-based medicine Meta-analysis and bioinformatics analysis.

METHODS

We conducted a Meta-analysis by searching Pubmed, Embase, and Web of Science databases to evaluate the correlation between herpes virus infection and increased risk of cerebral infarction. We obtained wild-type or mutant herpes virus latent infection-related brain tissue datasets from the GEO database and performed differential analysis to identify differentially expressed genes (DEGs) in the brain tissue after herpes virus latent infection. We further conducted WGCNA co-expression analysis on the cerebral infarction-related datasets from the GEO database to obtain key module genes and intersect them with the DEGs. We used ROC curve analysis to identify the key gene OTUD1 for predicting the occurrence of cerebral infarction and combined correlation and pathway enrichment analyses to identify the downstream pathways regulated by OTUD1.

RESULTS

Our meta-analysis revealed that herpes virus infection is associated with an increased risk of cerebral infarction. By integrating the differential analysis and WGCNA co-expression analysis of GEO chip data, we identified three key genes mediating cerebral infarction after herpes virus latent infection. ROC curve analysis identified the key gene OTUD1, and the correlation and pathway enrichment analyses showed that OTUD1 regulates the NF-κB signaling pathway to mediate cerebral infarction.

CONCLUSION

Herpes virus latent infection promotes cerebral infarction by activating the OTUD1/NF-κB signaling pathway.

A novel disulfidptosis and glycolysis related risk score signature for prediction of prognosis and ICI therapeutic responsiveness in colorectal cancer

Disulfidptosis is a newly-identified non-programmed cell death mode with tight associations with glucose metabolism. Elevated glycolysis is an important metabolic feature of tumor cells, which fulfills the energy requirement for their rapid growth and progression. Our present study determined to develop a disulfidptosis and glycolysis related gene (DGRG) risk score signature to predict the prognosis and ICI therapeutic responsiveness for CRC patients. First, the gene expression and clinical profiles for CRC patients were obtained from TCGA and GEO database. Using weighted gene co-expression network analysis, we identified hub genes showing the strongest correlations with both disulfidptosis and glycolysis activities. Next, a DGRG risk score signature was successfully developed through univariate and least absolute shrinkage and selection operator method Cox regression method. A DGRG risk score-based nomogram could further enhance the predictive performance. In addition, an array of systemic analysis was performed to unravel the correlation of DGRG risk score with tumor microenvironment. The results showed that CRC patients with low DGRG risk level had up-regulated immune cell infiltrations, enhanced metabolic activities and heightened gene mutation frequencies, while high risk patients was the opposite. Moreover, our present study identified low risk CRC patients as potential beneficiaries from immune checkpoint inhibitor (ICI) therapies. Our present work highlighted the potential utility of DGRG risk score signature in prognosis prediction and ICI responsiveness determination for CRC patients, which demonstrated promising clinical application value.

Development of cancer-associated fibroblast-related gene signature for predicting the survival and immunotherapy response in lung adenocarcinoma

The present study aims to construct a predictive model for prognosis and immunotherapy response in lung adenocarcinoma (LUAD). Transcriptome data were extracted from the Cancer Genome Atlas (TCGA), GSE41271, and IMvigor210. The weighted gene correlation network analysis was utilized to identify the hub modules related to immune/stromal cells. Then, univariate, LASSO, and multivariate Cox regression analyses were employed to develop a predictive signature based on genes of the hub module. Moreover, the association between the predictive signature and immunotherapy response was also investigated. As a result, seven genes (FGF10, SERINE2, LSAMP, STXBP5, PDE5A, GLI2, FRMD6) were screened to develop the cancer associated fibroblasts (CAFs)-related risk signature (CAFRS). LUAD patients with high-risk score underwent shortened Overall survival (OS). A strong correlation was found between CAFRS and immune infiltrations/functions. The gene set variation analysis showed that G2/M checkpoint, epithelial-mesenchymal transition, hypoxia, glycolysis, and PI3K-Akt-mTOR pathways were greatly enriched in the high-risk subgroup. Moreover, patients with higher risk score were less likely to respond to immunotherapy. A nomogram based on CAFRS and Stage presented a stronger predictive performance for OS than the single indicator. In conclusion, the CAFRS exhibited a potent predictive value for OS and immunotherapy response in LUAD.

A Novel Gene List Identifies Tumors with a Stromal-Mesenchymal Phenotype and Worse Prognosis in Gastric Cancer

BACKGROUND

Molecular biomarkers that predict disease progression can help identify tumor subtypes and shape treatment plans. In this study, we aimed to identify robust biomarkers of prognosis in gastric cancer based on transcriptomic data obtained from primary gastric tumors.

METHODS

Microarray, RNA sequencing, and single-cell RNA sequencing-based gene expression data from gastric tumors were obtained from public databases. Freshly frozen gastric tumors (n = 42) and matched FFPE (formalin-fixed, paraffin-embedded) (n = 40) tissues from a Turkish gastric cancer cohort were used for quantitative real-time PCR and immunohistochemistry-based assessments of gene expression, respectively.

RESULTS

A novel list of 20 prognostic genes was identified and used for the classification of gastric tumors into two major tumor subgroups with differential stromal gene expression ("Stromal-UP" (SU) and "Stromal-DOWN" (SD)). The SU group had a more mesenchymal profile with an enrichment of extracellular matrix-related gene sets and a poor prognosis compared to the SD group. Expression of the genes within the signature correlated with the expression of mesenchymal markers ex vivo. A higher stromal content in FFPE tissues was associated with shorter overall survival.

CONCLUSIONS

A stroma-rich, mesenchymal subgroup among gastric tumors identifies an unfavorable clinical outcome in all cohorts tested.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study.

Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score.

Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways.

Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.

CAF signature predicts the prognosis of colorectal cancer patients: A retrospective study based on bulk RNA sequencing and single-cell RNA sequencing data

The incidence rate and mortality rate of colorectal cancer (CRC) ranks third and second globally. Cancer-associated fibroblasts (CAFs) are the major constituent of the stromal cells in the tumor microenvironment (TME) and are closely associated with patients' prognoses. Our study intended to establish a prognostic model for CRC using hallmark genes of CAFs. The expression values of genes and clinicopathological characteristics of patients were enrolled from the cancer genome atlas database as well as the gene expression omnibus database. The single-cell RNA sequencing data were collected and analyzed in the deeply integrated human single-cell omics database and cancer single-cell expression map databases. The ESTIMATE algorithm was applied to access the infiltration levels of immune and stromal cells. The prognostic genes were selected by the Cox regression analysis and the prognostic signature was constructed by the least absolute shrinkage and selection operator algorithm. gene set enrichment analysis was used to explore the enriched gene sets. In this study, based on bulk RNA sequencing and single-cell RNA sequencing data, and we found that more CAFs were infiltrated in the tumor microenvironment and consisted of 3 subtypes. Then we constructed a prognostic signature for CRC using hallmark genes of CAFs and proved that this signature exhibited high values to predict the overall survival of CRC patients in independent training and validating cohorts. Besides, function enrichment analysis revealed that our prognostic model was significantly associated with immune regulation. Further analysis showed that the infiltrated levels of tumor-suppressing immune cells and the expression of higher immune checkpoint genes in CRC tissues were higher in patients with high-risk scores. Furthermore, immunohistochemistry analysis exhibited that these genes in our prognostic signature were markedly upregulated in CRC tissues. We first constructed a signature based on CAFs hallmark genes to predict the survival of CRC patients and further revealed that the tumor-suppressing microenvironment and dysregulated immune checkpoint genes in CRC tissues were partly responsible for the poor prognosis of patients.

SPOCK1 and POSTN are valuable prognostic biomarkers and correlate with tumor immune infiltrates in colorectal cancer

BACKGROUND

Immune cells and stromal cells in the tumor microenvironment play a vital role in the progression of colorectal cancer (CRC). The study aimed to screen valuable prognostic biomarkers in CRC based on stromal and immune scores.

METHOD

The ESTIMATE algorithm was used to calculate the immune and stromal scores of CRC samples in TCGA. Then samples were divided into high and low score groups based on the median value of the scores. Differentially expressed genes (DEGs) associated with immune and stromal scores were screened. WGCNA and univariate COX regression analysis were performed to further identify key prognostic genes. Analysis of scRNA-seq for CRC was used for verifying the main source of the key genes. The prognostic value of they was validated based on The Gene Expression Profiling Interactive Analysis and GSE17536 dataset. TIMER and CIBERSORT algorithms were applied to analyze the correlations among key genes and tumor-infiltrating immune cells. Several pairs of colon cancer tissue were used to be proven.

RESULT

1314 upregulated and 4 downregulated genes were identified, which were significantly enriched in immune-related biological processes and pathways. Among these DEGs, SPOCK1 and POSTN were identified as key prognostic genes and mainly expressed in cancer-associated fibroblasts for CRC. High expression of SPCOK1 and POSTN was associated with advanced clinical stage, T stage, N stage, and poor prognosis of CRC. The results from CIBERSORT and TIMER revealed that SPOCK1 and POSTN were associated with tumor-infiltrating immune cells, especially macrophages and neutrophils. Meanwhile, in several pairs of human colorectal tissue samples, SPOK1 and POSTN were found to be significantly overexpressed in colorectal tissue compared with para-cancer tissue, and macrophage surface markers CD68 (co-expressed by M1 and M2 macrophages) and CD206 (M2-specific macrophage expression) were also overexpressed in cancer tissue. Besides, SPOCK1 and POSTN expression were positively correlated with the expression of immune checkpoints.

CONCLUSION

Collectively, our results indicate that SPOCK1 and POSTN associated with CAF may be novel prognostic biomarkers in CRC and correlate with immune infiltrates.

Identification and characterization of a novel molecular classification incorporating oxidative stress and metabolism-related genes for stomach adenocarcinoma in the framework of predictive, preventive, and personalized medicine

BACKGROUND

Stomach adenocarcinoma (STAD) is one of the primary contributors to deaths that are due to cancer globally. At the moment, STAD does not have any universally acknowledged biological markers, and its predictive, preventive, and personalized medicine (PPPM) remains sufficient. Oxidative stress can promote cancer by increasing mutagenicity, genomic instability, cell survival, proliferation, and stress resistance pathways. As a direct and indirect result of oncogenic mutations, cancer depends on cellular metabolic reprogramming. However, their roles in STAD remain unclear.

METHOD

743 STAD samples from GEO and TCGA platforms were selected. Oxidative stress and metabolism-related genes (OMRGs) were acquired from the GeneCard Database. A pan-cancer analysis of 22 OMRGs was first performed. We categorized STAD samples by OMRG mRNA levels. Additionally, we explored the link between oxidative metabolism scores and prognosis, immune checkpoints, immune cell infiltration, and sensitivity to targeted drugs. A series of bioinformatics technologies were employed to further construct the OMRG-based prognostic model and clinical-associated nomogram.

RESULTS

We identified 22 OMRGs that could evaluate the prognoses of patients with STAD. Pan-cancer analysis concluded and highlighted the crucial part of OMRGs in the appearance and development of STAD. Subsequently, 743 STAD samples were categorized into three clusters with the enrichment scores being C2 (upregulated) > C3 (normal) > C1 (downregulated). Patients in C2 had the lowest OS rate, while C1 had the opposite. Oxidative metabolic score significantly correlates with immune cells and immune checkpoints. Drug sensitivity results reveal that a more tailored treatment can be designed based on OMRG. The OMRG-based molecular signature and clinical nomogram have good accuracy for predicting the adverse events of patients with STAD. Both transcriptional and translational levels of ANXA5, APOD, and SLC25A15 exhibited significantly higher in STAD samples.

CONCLUSION

The OMRG clusters and risk model accurately predicted prognosis and personalized medicine. Based on this model, high-risk patients might be identified in the early stage so that they can receive specialized care and preventative measures, and choose targeted drug beneficiaries to deliver individualized medical services. Our results showed oxidative metabolism in STAD and led to a new route for improving PPPM for STAD.

Prognostic value of genes related to cancer-associated fibroblasts in lung adenocarcinoma

BACKGROUND

Although it has been established that cancer-associated fibroblasts (CAFs) facilitate tumor development, the relationship between CAFs and the prognosis of patients with lung adenocarcinoma (LUAD) has not been extensively explored.

OBJECTIVE

This study was formulated to investigate the prognostic value of CAF-related genes in LUAD.

METHODS

Differential analysis was carried out with TCGA-LUAD dataset as the training set. By overlapping differentially expressed genes (DEGs) with genes associated with CAF, CAF-related DEGs specific to LUAD were obtained. A prognostic risk model was constructed by Lasso and Cox regression analysis, and samples were grouped according to median risk score. The efficacy of the model was accessed through survival curve and receiver operating characteristic curve (ROC) analyses, with the validation set for verification. Risk score combined with clinical factors was utilized for Cox analysis to verify the independence of the model, and a nomogram was drawn. GSEA was performed on different risk groups. Immunologic infiltration and tumor mutational burden were assessed in different risk groups.

RESULTS

Eleven feature genes including DLGAP5, KCNE2, UPK2, NPAS2, ARHGAP11A, ANGPTL4, ANLN, DKK1, SMUG1, C16orf74, and ACAD8 were identified, based on which a prognostic model was constructed. Risk score could predict the prognosis of LUAD patients and could be an independent prognostic factor for LUAD patients. GSEA outcomes displayed significant enrichment of genes in the high-risk group in the P53 SIGNALING PATHWAY. In comparison to the low-risk group, the high-risk group exhibited a decreased degree of immune infiltration and an elevated level of tumor mutational burden.

CONCLUSION

An 11-gene model was constructed based on CAF-related genes to predict LUAD prognosis. This model represented an independent prognostic factor for LUAD.

Identification of prognostic cancer-associated fibroblast markers in luminal breast cancer using weighted gene co-expression network analysis

Background

Cancer-associated fibroblasts (CAFs) play a pivotal role in cancer progression and are known to mediate endocrine and chemotherapy resistance through paracrine signaling. Additionally, they directly influence the expression and growth dependence of ER in Luminal breast cancer (LBC). This study aims to investigate stromal CAF-related factors and develop a CAF-related classifier to predict the prognosis and therapeutic outcomes in LBC.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to obtain mRNA expression and clinical information from 694 and 101 LBC samples, respectively. CAF infiltrations were determined by estimating the proportion of immune and cancer cells (EPIC) method, while stromal scores were calculated using the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify stromal CAF-related genes. A CAF risk signature was developed through univariate and least absolute shrinkage and selection operator method (LASSO) Cox regression model. The Spearman test was used to evaluate the correlation between CAF risk score, CAF markers, and CAF infiltrations estimated through EPIC, xCell, microenvironment cell populations-counter (MCP-counter), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. The TIDE algorithm was further utilized to assess the response to immunotherapy. Additionally, Gene set enrichment analysis (GSEA) was applied to elucidate the molecular mechanisms underlying the findings.

Results

We constructed a 5-gene prognostic model consisting of RIN2, THBS1, IL1R1, RAB31, and COL11A1 for CAF. Using the median CAF risk score as the cutoff, we classified LBC patients into high- and low-CAF-risk groups and found that those in the high-risk group had a significantly worse prognosis. Spearman correlation analyses demonstrated a strong positive correlation between the CAF risk score and stromal and CAF infiltrations, with the five model genes showing positive correlations with CAF markers. In addition, the TIDE analysis revealed that high-CAF-risk patients were less likely to respond to immunotherapy. Gene set enrichment analysis (GSEA) identified significant enrichment of ECM receptor interaction, regulation of actin cytoskeleton, epithelial-mesenchymal transition (EMT), and TGF-β signaling pathway gene sets in the high-CAF-risk group patients.

Conclusion

The five-gene prognostic CAF signature presented in this study was not only reliable for predicting prognosis in LBC patients, but it was also effective in estimating clinical immunotherapy response. These findings have significant clinical implications, as the signature may guide tailored anti-CAF therapy in combination with immunotherapy for LBC patients.

Cancer-Associated Fibroblast Risk Model for Prediction of Colorectal Carcinoma Prognosis and Therapeutic Responses

Colorectal carcinoma (CRC) is a malignant tumor of the digestive system. Cancer-associated fibroblasts (CAFs) are important cellular elements in the tumor microenvironment of CRC, which contribute to CRC progression and immune escape. To predict the survival outcome and therapeutic responses of CRC patients, we identified genes connected with stromal CAF and generated a risk model. In this study, we used multiple algorithms to reveal CAF-related genes in the Gene Expression Omnibus and The Cancer Genome Atlas datasets and construct a risk model composed by prognostic CAF-associated genes. Then, we evaluated whether the risk score could predict CAF infiltrations and immunotherapy in CRC and confirmed the expression of the risk model in CAFs. Our results showed that CRC patients with high CAF infiltrations and stromal score had worse prognosis than those with low-CAF infiltrations and stromal score. We obtained 88 stromal CAF-associated hub-genes and generated a CAF risk model consisting of ZNF532 and COLEC12. Compared with low-risk group, the overall survival in high-risk group was shorter. The relationship between risk score, ZNF532 and COLEC12, and stromal CAF infiltrations and CAF markers was positive. In addition, the effect of immunotherapy in the high-risk group was not as good as that in the low-risk group. Patients with the high-risk group were enriched in chemokine signaling pathway, cytokine-cytokine receptor interaction, and focal adhesion. Finally, we confirmed that the expressions of ZNF532 and COLEC12 in risk model were widely distributed in fibroblasts of CRC, and the expression levels were higher in fibroblasts than CRC cells. In conclusion, the prognostic CAF signature of ZNF532 and COLEC12 can be applied not only to predict the prognosis of CRC patients but also to evaluate the immunotherapy response in CRC patients, and these findings provide the possibility for further development of individualized treatment for CRC.

Construction of a co-expression network and prediction of metastasis markers in colorectal cancer patients with liver metastasis

BACKGROUND

Colorectal cancer (CRC) is a common global malignancy associated with high invasiveness, high metastasis, and poor prognosis. CRC commonly metastasizes to the liver, where the treatment of metastasis is both difficult and an important topic in current CRC management.

METHODS

Microarrays data of human CRC with liver metastasis (CRCLM) were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database to identify potential key genes. Differentially expressed (DE) genes (DEGs) and DEmiRNAs of primary CRC tumor tissues and metastatic liver tissues were identified. Microenvironment Cell Populations (MCP)-counter was used to estimate the abundance of immune cells in the tumor micro-environment (TME), and weighted gene correlation network analysis (WGCNA) was used to construct the co-expression network analysis. Gene Ontology and Kyoto Encyclopaedia of Gene and Genome (KEGG) pathway enrichment analyses were conducted, and the protein-protein interaction (PPI) network for the DEGs were constructed and gene modules were screened.

RESULTS

Thirty-five pairs of matched colorectal primary cancer and liver metastatic gene expression profiles were screened, and 610 DEGs (265 up-regulated and 345 down-regulated) and 284 DEmiRNAs were identified. The DEGs were mainly enriched in the complement and coagulation cascade pathways and renin secretion. Immune infiltrating cells including neutrophils, monocytic lineage, and cancer-associated fibroblasts (CAFs) differed significantly between primary tumor tissues and metastatic liver tissues. WGCN analysis obtained 12 modules and identified 62 genes with significant interactions which were mainly related to complement and coagulation cascade and the focal adhesion pathway. The best subset regression analysis and backward stepwise regression analysis were performed, and eight genes were determined, including F10, FGG, KNG1, MBL2, PROC, SERPINA1, CAV1, and SPP1. Further analysis showed four genes, including FGG, KNG1, CAV1, and SPP1 were significantly associated with CRCLM.

CONCLUSIONS

Our study implies complement and coagulation cascade and the focal adhesion pathway play a significant role in the development and progression of CRCLM, and FGG, KNG1, CAV1, and SPP1 may be metastatic markers for its early diagnosis.

Biomarker Genes Discovery of Alzheimer’s Disease by Multi-Omics-Based Gene Regulatory Network Construction of Microglia

Microglia, the major immune cells in the brain, mediate neuroinflammation, increased oxidative stress, and impaired neurotransmission in Alzheimer’s disease (AD), in which most AD risk genes are highly expressed. In microglia, due to the limitations of current single-omics data analysis, risk genes, the regulatory mechanisms, the mechanisms of action of immune responses and the exploration of drug targets for AD immunotherapy are still unclear. Therefore, we proposed a method to integrate multi-omics data based on the construction of gene regulatory networks (GRN), by combining weighted gene co-expression network analysis (WGCNA) with single-cell regulatory network inference and clustering (SCENIC). This enables snRNA-seq data and bulkRNA-seq data to obtain data on the deeper intermolecular regulatory relationships, related genes, and the molecular mechanisms of immune-cell action. In our approach, not only were central transcription factors (TF) STAT3, CEBPB, SPI1, and regulatory mechanisms identified more accurately than with single-omics but also immunotherapy targeting central TFs to drugs was found to be significantly different between patients. Thus, in addition to providing new insights into the potential regulatory mechanisms and pathogenic genes of AD microglia, this approach can assist clinicians in making the most rational treatment plans for patients with different risks; it also has significant implications for identifying AD immunotherapy targets and targeting microglia-associated immune drugs.

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.

Identification of a Gene Signature of Cancer-Associated Fibroblasts to Predict Prognosis in Ovarian Cancer

Ovarian cancer (OvCa) is one of the most widespread malignant tumors, which has the highest morbidity and unsatisfactory clinical outcomes among all gynecological malignancies in the world. Previous studies found that cancer-associated fibroblasts (CAFs) play significant roles in tumor growth, progression, and chemoresistance. In the current research, weighted gene co-expression network analysis (WGCNA), univariable COX regression, and the least absolute shrinkage and selection operator (LASSO) analysis were applied to recognize CAF-specific genes. After multiple bioinformatic analyses, four genes (AXL, GPR176, ITGBL1, and TIMP3) were identified as OvCa-specific CAF markers and used to construct the prognostic signature (CAFRS). Furthermore, the specificity of the four genes' expression was further validated at the single-cell level, which was high-selectively expressed in CAFs. In addition, our results showed that CAFRS is an independent significant risk factor affecting the clinical outcomes of OvCa patients. Meanwhile, patients with higher CAFRS were more likely to establish chemoresistance to platinum. Besides, the CAFRS were notably correlated with well-known signal pathways that were related to tumor progression. In summary, our study identifies four CAF-specific genes and constructs a novel prognostic signature, which may provide more insights into precise prognostic assessment in OvCa.

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

Cancer-associated fibroblasts (CAFs) are a major contributor to tumor stromal crosstalk in the tumor microenvironment (TME) and boost tumor progression by promoting angiogenesis and lymphangiogenesis. This study aimed to identify prognostic genes associated with CAFs that lead to high morbidity and mortality in ovarian cancer (OC) patients. We performed bioinformatics analysis in 16 multicenter studies (2,742 patients) and identified CAF-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify COL16A1, COL5A2, GREM1, LUM, SRPX, and TIMP3 and construct a prognostic signature. Subsequently, a series of bioinformatics algorithms indicated risk stratification based on the above signature, suggesting that high-risk patients have a worse prognosis, weaker immune response, and lower tumor mutational burden (TMB) status but may be more sensitive to routine chemotherapeutic agents. Finally, we characterized prognostic markers using cell lines, immunohistochemistry, and single-cell sequencing. In conclusion, these results suggest that the CAF-related signature may be a novel pretreatment guide for anti-CAFs, and prognostic markers in CAFs may be potential therapeutic targets to inhibit OC progression.

Curcuminoids as Modulators of EMT in Invasive Cancers: A Review of Molecular Targets With the Contribution of Malignant Mesothelioma Studies

Curcuminoids, which include natural acyclic diarylheptanoids and the synthetic analogs of curcumin, have considerable potential for fighting against all the characteristics of invasive cancers. The epithelial-to-mesenchymal transition (EMT) is a fundamental process for embryonic morphogenesis, however, the last decade has confirmed it orchestrates many features of cancer invasiveness, such as tumor cell stemness, metabolic rewiring, and drug resistance. A wealth of studies has revealed EMT in cancer is in fact driven by an increasing number of parameters, and thus understanding its complexity has now become a cornerstone for defining future therapeutic strategies dealing with cancer progression and metastasis. A specificity of curcuminoids is their ability to target multiple molecular targets, modulate several signaling pathways, modify tumor microenvironments and enhance the host’s immune response. Although the effects of curcumin on these various parameters have been the subject of many reviews, the role of curcuminoids against EMT in the context of cancer have never been reviewed so far. This review first provides an updated overview of all EMT drivers, including signaling pathways, transcription factors, non-coding RNAs (ncRNAs) and tumor microenvironment components, with a special focus on the most recent findings. Secondly, for each of these drivers the effects of curcumin/curcuminoids on specific molecular targets are analyzed. Finally, we address some common findings observed between data reported in the literature and the results of investigations we conducted on experimental malignant mesothelioma, a model of invasive cancer representing a useful tool for studies on EMT and cancer.

Cancer Stem Cells and the Tumor Microenvironment in Gastric Cancer

Gastric cancer (GC) remains one of the leading causes of cancer-related death worldwide. Cancer stem cells (CSCs) might be responsible for tumor initiation, relapse, metastasis and treatment resistance of GC. The tumor microenvironment (TME) comprises tumor cells, immune cells, stromal cells and other extracellular components, which plays a pivotal role in tumor progression and therapy resistance. The properties of CSCs are regulated by cells and extracellular matrix components of the TME in some unique manners. This review will summarize current literature regarding the effects of CSCs and TME on the progression and therapy resistance of GC, while emphasizing the potential for developing successful anti-tumor therapy based on targeting the TME and CSCs.

Cancer Progression Mediated by CAFs Relating to HCC and Identification of Genetic Characteristics Influencing Prognosis

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies, and although there are several treatment options, the overall results are not satisfactory. Cancer-associated fibroblasts (CAFs) can promote cancer progression through various mechanisms.

METHODS

HCC-associated mRNA data were sourced from The Cancer Genome Atlas database (TCGA) and International Cancer Genome Consortium (ICGC) database. First, the differentially expressed CAF-related genes (CAF-DEGs) were acquired by difference analysis and weighted gene coexpression network analysis (WGCNA). Moreover, a CAF-related risk model was built by Cox analysis. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were utilized to evaluate the validity of this risk model. Furthermore, enrichment analysis of differentially expressed genes (DEGs) between the high- and low-risk groups was executed to explore the functions relevant to the risk model. Furthermore, this study compared the differences in immune infiltration, immunotherapy, and drug sensitivity between the high- and low-risk groups. Finally, we verified the mRNA expression levels of selected prognostic genes by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

107 CAF-DEGs were identified in the HCC samples, and five prognosis-related genes (ACTA2, IGJ, CTHRC1, CXCL12, and LAMB1) were obtained by Cox analysis and utilized to build a CAF-related risk model. K-M analysis illustrated a low survival in the high-risk group, and ROC curves revealed that the risk model could accurately predict the 1-, 3-, and 5-year overall survival (OS) of HCC patients. In addition, Cox analysis demonstrated that the risk score was an independent prognostic factor. Enrichment analysis illustrated that DEGs between the high- and low-risk groups were related to immune response, amino acid metabolism, and fatty acid metabolism. Furthermore, risk scores were correlated with the tumor microenvironment, CAF scores, and TIDE scores, and CAF-related marker genes were positively correlated with all five model genes. Notably, the risk model was relevant to the sensitivity of chemotherapy drugs. Finally, the results of qRT-PCR demonstrated that the expression levels of 5 model genes were in accordance with the analysis.

CONCLUSION

A CAF-related risk model based on ACTA2, IGJ, CTHRC1, CXCL12, and LAMB1 was built and could be utilized to predict the prognosis and treatment of HCC.