Transcriptome-Based Network Analysis Unveils Eight Immune-Related Genes as Molecular Signatures in the Immunomodulatory Subtype of Triple-Negative Breast Cancer

Towards key genes identification for breast cancer survival risk with neural network models

Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurrence, which endangers the health and life of patients. While more and more data have been available, how to leverage the gene expression data to predict the survival risk of cancer patients and identify key genes has become a hot topic for cancer research. Therefore, in this work, we investigate the gene expression and clinical data of breast cancer patients, specifically a novel framework is proposed focusing on the survival risk classification and key gene identification task. We firstly combine the differential expression and univariate Cox regression analysis to achieve dimensional reduction of gene expression data. The median survival time is subsequently proposed as the risk classification threshold and a learning model based on neural network is trained to classify the survival risk of patients. Innovatively, in this work, the activation region visualization technology is selected as the identification tool, which identify 20 key genes related to the survival risk of breast cancer patients. We further analyze the gene function of these 20 key genes based on STRING database. It is critical to learn that, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Importantly, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Our work accomplishes the objective of proposing a targeted approach to enhancing the survival analysis and therapeutic strategies in breast cancer through advanced computational techniques and gene analysis.

Value of altered methylation patterns of genes RANBP3, LCP2 and GRAP2 in cfDNA in breast cancer diagnosis

Background

The purpose of this study was to investigate the potential of plasma cfDNA methylation patterns in reflecting tumour methylation changes, focusing on three candidate sites, cg02469161, cg11528914, and cg20131654. These sites were selected for verification, with a particular emphasis on their association with breast cancer.

Methods

We conducted a comprehensive analysis of 850k whole-methylation sequencing data to identify potential markers for breast cancer detection. Subsequently, we investigated the methylation status of the genes Ran-binding protein 3 (RANBP3), Lymphocyte cytoplasmic protein 2 (LCP2), and GRB2 related adaptor protein 2 (GRAP2), situated at the specified sites, using cancer and canceradjacent tissues from 17 breast cancer patients. We also examined the methylation patterns in different molecular subtypes and pathological grades of breast cancer. Additionally, we compared the methylation levels of these genes in plasma cfDNA to their performance in tissues.

Results

Our analysis revealed that RANBP3, LCP2, and GRAP2 genes exhibited significant methylation differences between cancer and cancer-adjacent tissues. In breast cancer, these genes displayed diagnostic efficiencies of 91.0%, 90.6%, and 92.2%, respectively. Notably, RANBP3 showed a tendency towards lower methylation in HR+ breast cancer, and LCP2 methylation was correlated with tumour malignancy. Importantly, the methylation levels of these three genes in plasma cfDNA closely mirrored their tissue counterparts, with diagnostic efficiencies of 83.3%, 83.9%, and 77.6% for RANBP3, LCP2, and GRAP2, respectively.

Conclusions

Our findings propose that the genes RANBP3, LCP2, and GRAP2, located at the identified methylation sites, hold significant potential as molecular markers in blood for the supplementary diagnosis of breast cancer. This study lays the groundwork for a more in-depth investigation into the changes in gene methylation patterns in circulating free DNA (cfDNA) for the early detection not only of breast cancer but also for various other types of cancer.

Triple Negative Breast Cancer: Molecular Subtype-Specific Immune Landscapes with Therapeutic Implications

Triple Negative Breast Cancer (TNBC) is characterized by distinct molecular subtypes with unique biological and clinical features. This systematic review aimed to identify articles examining the differences in the tumor immune microenvironment (TIME) across different TNBC molecular subtypes. Six studies meeting inclusion criteria were analyzed, utilizing gene expression profiling and bioinformatic analyses to classify TNBC samples into molecular subtypes, as well as immunohistochemistry and cell deconvolution methods to characterize the TIME. Results revealed significant heterogeneity in immune cell composition among TNBC subtypes, with the immunomodulatory (IM) subtype demonstrating robust immune infiltration, composed mainly of adaptive immune cells along with an increased density of CTLA-4+ and PD-1+ TILs, high PD-L1 tumor cell expression, and upregulation of FOXP3+ Tregs. A more immunosuppressive TIME with a predominance of innate immune cells and lower levels of tumor-infiltrating lymphocytes (TILs) was observed in luminal androgen receptor (LAR) tumors. In mesenchymal stem-like (MSL) tumors, the TIME was mainly composed of innate immune cells, with a high number of M2 tumor-associated macrophages (TAMs), while the BL and M tumors displayed poor adaptive and innate immune responses, indicating an "immune-cold" phenotype. Differential activation of signaling pathways, genomic diversity, and metabolic reprogramming were identified as contributors to TIME heterogeneity. Understanding this interplay is crucial for tailoring therapeutic strategies, especially regarding immunotherapy.

T-reg transcriptomic signatures identify response to check-point inhibitors

Regulatory T cells (Tregs) is a subtype of CD4+ T cells that produce an inhibitory action against effector cells. In the present work we interrogated genomic datasets to explore the transcriptomic profile of breast tumors with high expression of Tregs. Only 0.5% of the total transcriptome correlated with the presence of Tregs and only four transcripts, BIRC6, MAP3K2, USP4 and SMG1, were commonly shared among the different breast cancer subtypes. The combination of these genes predicted favorable outcome, and better prognosis in patients treated with checkpoint inhibitors. Twelve up-regulated genes coded for proteins expressed at the cell membrane that included functions related to neutrophil activation and regulation of macrophages. A positive association between MSR1 and CD80 with macrophages in basal-like tumors and between OLR1, ABCA1, ITGAV, CLEC5A and CD80 and macrophages in HER2 positive tumors was observed. Expression of some of the identified genes correlated with favorable outcome and response to checkpoint inhibitors: MSR1, CD80, OLR1, ABCA1, TMEM245, and ATP13A3 predicted outcome to anti PD(L)1 therapies, and MSR1, CD80, OLR1, ANO6, ABCA1, TMEM245, and ATP13A3 to anti CTLA4 therapies, including a subgroup of melanoma treated patients. In this article we provide evidence of genes strongly associated with the presence of Tregs that modulates the response to check point inhibitors.

The molecular subtyping and precision medicine in triple-negative breast cancer---based on Fudan TNBC classification

Triple-negative breast cancer (TNBC) is widely recognized as the most aggressive form of breast cancer, occurring more frequently in younger patients and characterized by high heterogeneity, early distant metastases and poor prognosis. Multiple treatment options have failed to achieve the expected therapeutic effects due to the lack of clear molecular targets. Based on genomics, transcriptomics and metabolomics, the multi-omics analysis further clarifies TNBC subtyping, which provides a greater understanding of tumour heterogeneity and targeted therapy sensitivity. For instance, the luminal androgen receptor subtype (LAR) exhibits responsiveness to anti-AR therapy, and the basal-like immune-suppressed subtype (BLIS) tends to benefit from poly (ADP-ribose) polymerase inhibitors (PARPis) and anti-angiogenic therapy. The efficacy of multi-dimensional combination therapy holds immense importance in guiding personalized and precision medicine for TNBC. This review offers a systematic overview of recent FuDan TNBC molecular subtyping and its role in the instruction of clinical precision therapy.

Integration of exome-seq and mRNA-seq using DawnRank, identified genes involved in innate immunity as drivers of breast cancer in the Indian cohort

Genetic heterogeneity influences the prognosis and therapy of breast cancer. The cause of disease progression varies and can be addressed individually. To identify the mutations and their impact on disease progression at an individual level, we sequenced exome and transcriptome from matched normal-tumor samples. We utilised DawnRank to prioritise driver genes and identify specific mutations in Indian patients. Mutations in the C3 and HLA genes were identified as drivers of disease progression, indicating the involvement of the innate immune system. We performed immune profiling on 16 matched normal/tumor samples using CIBERSORTx. We identified CD8+ve T cells, M2 macrophages, and neutrophils to be enriched in luminal A and T cells CD4+naïve, natural killer (NK) cells activated, T follicular helper (Tfh) cells, dendritic cells activated, and neutrophils in triple-negative breast cancer (TNBC) subtypes. Weighted gene co-expression network analysis (WGCNA) revealed activation of T cell-mediated response in ER positive samples and Interleukin and Interferons in ER negative samples. WGCNA analysis also identified unique pathways for each individual, suggesting that rare mutations/expression signatures can be used to design personalised treatment.

Comprehensively analysis of immunophenotyping signature in triple-negative breast cancer patients based on machine learning

Immunotherapy is a promising strategy for triple-negative breast cancer (TNBC) patients, however, the overall survival (OS) of 5-years is still not satisfactory. Hence, developing more valuable prognostic signature is urgently needed for clinical practice. This study established and verified an effective risk model based on machine learning methods through a series of publicly available datasets. Furthermore, the correlation between risk signature and chemotherapy drug sensitivity were also performed. The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that IL18R1, BTN3A1, CD160, CD226, IL12B, GNLY and PDCD1LG2 are key genes that may affect immune typing of TNBC patients. The risk signature plays a robust ability in prognosis prediction compared with other clinicopathological features in TNBC patients. In addition, the effect of our constructed risk model on immunotherapy response was superior to TIDE results. Finally, high-risk groups were more sensitive to MR-1220, GSK2110183 and temsirolimus, indicating that risk characteristics could predict drug sensitivity in TNBC patients to a certain extent. This study proposes an immunophenotype-based risk assessment model that provides a more accurate prognostic assessment tool for patients with TNBC and also predicts new potential compounds by performing machine learning algorithms.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

Genetic Heterogeneity, Tumor Microenvironment and Immunotherapy in Triple-Negative Breast Cancer

Heterogeneity of triple-negative breast cancer is well known at clinical, histopathological, and molecular levels. Genomic instability and greater mutation rates, which may result in the creation of neoantigens and enhanced immunogenicity, are additional characteristics of this breast cancer type. Clinical outcome is poor due to early age of onset, high metastatic potential, and increased likelihood of distant recurrence. Consequently, efforts to elucidate molecular mechanisms of breast cancer development, progression, and metastatic spread have been initiated to improve treatment options and improve outcomes for these patients. The extremely complex and heterogeneous tumor immune microenvironment is made up of several cell types and commonly possesses disorganized gene expression. Altered signaling pathways are mainly associated with mutated genes including p53, PIK3CA, and MAPK, and which are positively correlated with genes regulating immune response. Of note, particular immunity-associated genes could be used in prognostic indexes to assess the most effective management. Recent findings highlight the fact that long non-coding RNAs also play an important role in shaping tumor microenvironment formation, and can mediate tumor immune evasion. Identification of molecular signatures, through the use of multi-omics approaches, and effector pathways that drive early stages of the carcinogenic process are important steps in developing new strategies for targeted cancer treatment and prevention. Advances in immunotherapy by remodeling the host immune system to eradicate tumor cells have great promise to lead to novel therapeutic strategies. Current research is focused on combining immune checkpoint inhibition with chemotherapy, PARP inhibitors, cancer vaccines, or natural killer cell therapy. Targeted therapies may improve therapeutic response, eliminate therapeutic resistance, and improve overall patient survival. In the future, these evolving advancements should be implemented for personalized medicine and state-of-art management of cancer patients.

The Pharmacological Mechanism of Curcumin against Drug Resistance in Non-Small Cell Lung Cancer: Findings of Network Pharmacology and Bioinformatics Analysis

The pharmacological mechanism of curcumin against drug resistance in non-small cell lung cancer (NSCLC) remains unclear. This study aims to summarize the genes and pathways associated with curcumin action as an adjuvant therapy in NSCLC using network pharmacology, drug-likeness, pharmacokinetics, functional enrichment, protein-protein interaction (PPI) analysis, and molecular docking. Prognostic genes were identified from the curcumin-NSCLC intersection gene set for the following drug sensitivity analysis. Immunotherapy, chemotherapy, and targeted therapy sensitivity analyses were performed using external cohorts (GSE126044 and IMvigor210) and the CellMiner database. 94 curcumin-lung adenocarcinoma (LUAD) hub targets and 41 curcumin-lung squamous cell carcinoma (LUSC) hub targets were identified as prognostic genes. The anticancer effect of curcumin was observed in KEGG pathways involved with lung cancer, cancer therapy, and other cancers. Among the prognostic curcumin-NSCLC intersection genes, 20 LUAD and 8 LUSC genes were correlated with immunotherapy sensitivity in the GSE126044 NSCLC cohort; 30 LUAD and 13 LUSC genes were associated with immunotherapy sensitivity in the IMvigor210 cohort; and 12 LUAD and 13 LUSC genes were related to chemosensitivity in the CellMiner database. Moreover, 3 LUAD and 5 LUSC genes were involved in the response to targeted therapy in the CellMiner database. Curcumin regulates drug sensitivity in NSCLC by interacting with cell cycle, NF-kappa B, MAPK, Th17 cell differentiation signaling pathways, etc. Curcumin in combination with immunotherapy, chemotherapy, or targeted drugs has the potential to be effective for drug-resistant NSCLC. The findings of our study reveal the relevant key signaling pathways and targets of curcumin as an adjuvant therapy in the treatment of NSCLC, thus providing pharmacological evidence for further experimental research.

Bioinformatics analysis of the key genes in osteosarcoma metastasis and immune invasion

BACKGROUND

This study sought to identify potential key genes for osteosarcoma metastasis and analyze their immune infiltration patterns using bioinformatic methods.

METHODS

We obtained transcriptomic data related to osteosarcoma and osteosarcoma with metastasis from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) and The Gene Expression Omnibus (GEO) databases and identified the differentially expressed genes (DEGs). We also identified potential key genes for osteosarcoma metastasis by a protein-protein interaction network analysis, and we conducted a Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify the core genes for prognosis, immune cell infiltration, and drug sensitivity, and the risk prediction and prognosis models of metastasis were constructed.

RESULTS

By comparing the transcriptome data of osteosarcomas without metastasis and those with metastasis, a total of 19 core DEGs were identified, and the GO and KEGG analyses revealed an association between these DEGs and the regulation of cell division, secretory granule lumen, the Ras-associated protein 1 (Rap1) signaling pathway, and the mitogen-activated protein kinase (MAPK) signaling pathway. Compared with other immune cells, macrophage infiltration was predominant in osteosarcoma samples with metastatic osteosarcoma, and insulin-like growth factors-1 (IGF1) and myelocytomatosis protein 2 (MYC2) genes were predicted to more than 50 targeted therapeutic agents. A metastasis prediction model with 5 genes [i.e., ecotropic viral integration site 2B (EVI2B), CCAAT/enhancer binding protein (CEBPA), lymphocyte cytosolic protein 2 (LCP2), selectin L (SELL), and Niemann-Pick disease, type C2A (NPC2A)], and a prognostic model with 4 genes [i.e., insulin-like growth factors-2 (IGF2), cathepsin O (CTSO), Niemann-Pick disease, type C2 (NPC2), and amyloid beta (A4) precursor protein-binding, family B, member 1 interacting protein (APBB1IP)] were developed.

CONCLUSIONS

We constructed a metastasis prediction model with 5 genes (i.e., EVI2B, CEBPA, LCP2, SELL, and NPC2A), and a prognostic model with 4 genes (i.e., IGF2, CTSO, NPC2, and APBB1IP) that may be potential biomarkers for osteosarcoma metastasis. Macrophages are the predominant immune infiltrating cells in osteosarcoma metastasis and may provide a new direction for the treatment of osteosarcoma.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Androgen Receptor-Positive TNBC

As the most invasive and lethal subtype of breast cancer (BC), triple-negative breast carcinoma (TNBC) is of increasing interest. However, the androgen receptor (AR) still has an unclear role in TNBC. The current study is aimed at testing the diagnostic and therapeutic performance of novel biomarkers for AR-positive TNBC. The GSE76124 dataset was analyzed by combining WGCNA and other bioinformatics methods. Subsequently, function enrichment analysis was applied to identify the relationships between these differential expression genes (DEGs). Subsequently, the protein-protein interaction network was established, and the hub genes were identified by Cytoscape software. Eventually, the miRNA-hub gene modulate network was developed and the Drug-Gene Interaction Database (DGIdb) was applied to verify the potential drugs for AR-positive TNBC. In the current research, 88 DEGs in total were selected from the intersection of the purple module genes identified by WGCNA and limma package. TFF1, FOXA1, ESR1, AGR2, TFF3, AGR3, GATA3, XBP1, SPDEF, and TOX3 were selected as hub genes by the MCC method, which were all upregulated. The survival analysis suggested that TFF1 was the only one related to significant lower survival rate in TNBC. Ultimately, hsa-miR-520g-3p and hsa-miR-520h were found taking part in the regulation of TFF1, and 2 small molecules were identified as the potential targets for AR-positive TNBC treatment. As a result, our study suggested that hsa-miR-520g-3p, hsa-miR-520h, and TFF1 might have significant potential values for AR-positive TNBC diagnosis and prognosis prediction. TFF1, hsa-miR-520g-3, and hsa-miR-520h may serve as the novel therapeutic targets, and our findings offer further insights into the therapy of AR-positive TNBC.

Comprehensive analysis of GINS subunits prognostic value and ceRNA network in sarcoma

Background: The GINS complex, composed of GINS1/2/3/4 subunits, is an essential structure of Cdc45-MCM-GINS (CMG) helicase and plays a vital role in establishing the DNA replication fork and chromosome replication. Meanwhile, GINS genes have been associated with the poor prognosis of various malignancies. However, the abnormal expression of GINS genes and their diagnostic and prognostic value in sarcomas (SARC) remain unclear.

Methods: Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier Plotter, Cancer cell line encyclopedia (CCLE), The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN), R studio, and Tumor Immune Estimation Resource (TIMER) were used to analyze the expression profiles, prognostic value, biological function, ceRNA, and immune infiltration associated with GINS genes in sarcomas.

Results: We found that GINS1/2/3/4 genes exhibited significantly upregulated transcription levels in SARC samples compared to non-tumor tissues and exhibited high expression levels in sarcoma cell lines. In addition, SARC patients with increased expression levels of GINS1/2/3/4 showed poorer survival rates. Immune infiltration analysis showed that GINS subunits were closely associated with the infiltration of immune cells in sarcomas.

Conclusion: Our research identified GINS subunits as potential diagnostic and prognostic biological targets in SARC and elucidated their underlying effects in the genesis and progression of SARC. These results may provide new opportunities and research directions for targeted sarcoma therapy.

Comprehensive Analysis of the Prognostic Value and Immune Infiltration of Butyrophilin Subfamily 2/3 (BTN2/3) Members in Pan-Glioma

The BTN2/3 subfamilies are overexpressed in many cancers, including pan-glioma (low- and high-grade gliomas). However, the expression and prognosis of BTN2/3 subfamilies and tumor-infiltrating lymphocytes in pan-glioma remain unknown. In the present study, we systematically explored and validated the expression and prognostic value of BTN2/3 subfamily members in pan-glioma [The Cancer Genome Atlas–glioblastoma and low-grade glioma (TCGA-GBMLGG) merge cohort] using multiple public databases. We used clinical specimens for high-throughput verification and cell lines for qRT-PCR verification, which confirmed the expression profiles of BTN2/3 subfamilies. In addition, the function of the BTN2/3 subfamily members and the correlations between BTN2/3 subfamily expression and pan-glioma immune infiltration levels were investigated. We found that BTN2/3 subfamily members were rarely mutated. BTN2/3 subfamilies were overexpressed in pan-glioma; high expression of BTN2/3 subfamily members was correlated with poor prognosis. In addition, BTN2/3 subfamilies might positively regulate proliferation, and the overexpression of BTN2/3 subfamilies influenced cell cycle, differentiation, and glioma stemness. In terms of immune infiltrating levels, BTN2/3 subfamily expression was positively associated with CD4+ T-cell, B-cell, neutrophil, macrophage, and dendritic cell infiltrating levels. These findings suggest that BTN2/3 subfamily expression is correlated with prognosis and immune infiltration levels in glioma. Therefore, the BTN2/3 subfamilies can be used as biomarkers for pan-glioma and prognostic biomarkers for determining the prognosis and immune infiltration levels in pan-glioma.

Immunogenic Cell Death-Relevant Damage-Associated Molecular Patterns and Sensing Receptors in Triple-Negative Breast Cancer Molecular Subtypes and Implications for Immunotherapy

Objectives

Triple-negative breast cancer (TNBC) is defined as a highly aggressive type of breast cancer which lacks specific biomarkers and drug targets. Damage-associated molecular pattern (DAMP)-induced immunogenic cell death (ICD) may influence the outcome of immunotherapy for TNBC patients. This study aims to develop a DAMPs gene signature to classify TNBC patients and to further predict their prognosis and immunotherapy outcome.

Methods

We identified the DAMPs-associated subtypes of 330 TNBCs using K-means analysis. Differences in immune status, genomic alterations, and predicted immunotherapy outcome were compared among each subtype.

Results

A total of 330 TNBCs were divided into three subtypes according to DAMPs gene expression: the nuclear DAMPs subtype, featuring the upregulation of nuclear DAMPs; the inflammatory DAMPs subtype, characterized by the gene set enrichment of the adaptive immune system and cytokine signaling in the immune system; and the DAMPs-suppressed subtype, having the lowest level of ICD-associated DAMPs. Among them, the inflammatory subtype patients had the most favorable survival, while the DAMPs-suppressed subtype was associated with the worst prognosis. The DAMPs subtyping system was successfully validated in the TCGA cohort. Furthermore, we systemically revealed the genomic alterations among the three DAMPs subtypes. The inflammatory DAMPs subtype was predicted to have the highest response rate to immunotherapy, suggesting that the constructed DAMPs clustering had potential for immunotherapy efficacy prediction.

Conclusion

We established a novel ICD-associated DAMPs subtyping system in TNBC, and DAMPs expression might be a valuable biomarker for immunotherapy strategies. Our work could be helpful to the development of new immunomodulators and may contribute to the development of precision immunotherapy for TNBC.

GIMAP7 as a Potential Predictive Marker for Pan-Cancer Prognosis and Immunotherapy Efficacy

Background

Methods

Results

Conclusion

Lymphocyte cytosolic protein 2 is a novel prognostic marker in lung adenocarcinoma

OBJECTIVE

Lymphocyte cytosolic protein 2 (LCP2) is often ectopically expressed in various human tumors. However, the clinical significance and role of LCP2 in lung adenocarcinoma (LUAD) remain unclear. This study explored the prognostic significance of LCP2 in LUAD patients.

METHODS

LCP2 expression in LUAD tissues was analyzed using data from The Cancer Genome Atlas and Genotype-Tissue Expression databases. Western blotting was employed to detect LCP2 expression in LUAD. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to explore signaling pathways mediated by LCP2 co-regulatory genes. Immunohistochemistry was used to examine levels of LCP2 and programmed death ligand 1 (PD-L1) in 68 LUAD patients. Associations between LCP2 expression and clinicopathological features, prognoses, and PD-L1 levels among the LUAD in-patients were analyzed.

RESULTS

Among the 68 LUAD in-patients, LCP2 expression was correlated with clinical stage and lymph node metastasis. LUAD patients with high LCP2 expression were associated with increased overall survival. LCP2 expression may be associated with an enrichment of several immune functions. Moreover, our immunohistochemistry results demonstrated that LCP2 expression was positively correlated with PD-L1 expression in LUAD tissues.

CONCLUSIONS

In the study, LCP2 was found to be a favorable prognostic biomarker in LUAD patients.

Identification of prognostic biomarkers associated with tumor microenvironment in ceRNA network for esophageal squamous cell carcinoma: a bioinformatics study based on TCGA database

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer in the world with high incidence rate and poor prognosis. Infiltrated immune and stromal cells are vital components of tumor microenvironment (TME) and have a significant impact on the progression of ESCC. The competitive endogenous RNA (ceRNA) hypothesis has been proved important in the molecular biological mechanisms of tumor development. However, there are few studies on the relationship between ceRNA and ESCC TME.

METHODS

The proportion of tumor-infiltrating immune cells and the amount of stromal and immune cells in ESCC cases were calculated from The Cancer Genome Atlas database using the CIBERSORT and ESTIMATE calculation methods. After stratified identification of differentially expressed genes, WGCNA and miRNA prediction system were applied to construct ceRNA network. Finally, PPI network and survival analysis were selected to discriminate prognostic signature. And the results were verified in two independent groups from Gene Expression Omnibus and Lanzhou, China.

RESULTS

We found that high Stromal and ESTIMATE scores were significantly associated with poor overall survival. Three TME-related key prognostic genes were screened, namely, LCP2, CD86, SLA. And the expression of them was significantly correlated with infiltrated immunocytes. It is also found that ESTIMATE Score and the expression of CD86 were both related to TNM system of ESCC.

CONCLUSIONS

We identified three novel TME-related prognostic markers and their lncRNA-miRNA-mRNA pathway in ESCC patients, which may provide new strategies for the targeted therapy.

Dissecting the Role of N6-Methylandenosine-Related Long Non-coding RNAs Signature in Prognosis and Immune Microenvironment of Breast Cancer

Breast cancer (BC) represents a molecularly and clinically heterogeneous disease. Recent progress in immunotherapy has provided a glimmer of hope for several BC subtypes. The relationship between N6-methyladenosine (m6A) modification and long non-coding RNAs (LncRNAs) is still largely unexplored in BC. Here, with the intention to dissect the landscape of m6A-related lncRNAs and explore the immunotherapeutic value of the m6A-related lncRNA signature, we identified m6A-related lncRNAs by co-expression analysis from The Cancer Genome Atlas (TCGA) and stratified BC patients into different subgroups. Furthermore, we generated an m6A-related lncRNA prognostic signature. Four molecular subtypes were identified by consensus clustering. Cluster 3 preferentially had favorable prognosis, upregulated immune checkpoint expression, and high level of immune cell infiltration. Twenty-one m6A-related lncRNAs were applied to construct the m6A-related lncRNA model (m6A-LncRM). Survival analysis and receiver operating characteristic (ROC) curves further confirmed the prognostic value and prediction performance of m6A-LncRM. Finally, high- and low-risk BC subgroups displayed significantly different clinical features and immune cell infiltration status. Overall, our study systematically explored the prognostic value of the m6A-related LncRNAs and identified a high immunogenicity BC subtype. The proposed m6A-related LncRNA model might serve as a robust prognostic signature and attractive immunotherapeutic targets for BC treatment.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Predicting Panel of Metabolism and Immune-Related Genes for the Prognosis of Human Ovarian Cancer

Objective

Ovarian cancer (OC) is a high deadly gynecologic cancer with a poor prognosis. The identification of genomic aberrations could predict the clinical prognosis of OC patients and may eventually develop new therapeutic strategies in the future. The purpose of this study is to create comprehensive co-expressed gene networks correlated with metabolism and the immune process of OC.

Methods

The transcriptome profiles of TCGA OC datasets and GSE26193 datasets were analyzed. The mRNA expression level, hub genomic alteration, patient’s survival status, and tumor cell immune microenvironment of metabolism-related genes were analyzed from TCGA, GTEX, Oncomine, Kaplan-Meier Plotter, cBioPortal, TIMER, ESTIMATE, and CIBERSORT databases. We further validated the mRNA and protein expression levels of these hub genes in OC cell lines and tissues using qRT-PCR and immunohistochemistry.

Results

The LASSO-Cox regression analyses unveiled seven differently expressed metabolism-related genes, including GFPT2, DGKD, ACACB, ACSM3, IDO1, TPMT, and PGP. The Cox regression risk model could be served as an independent marker to predict the overall clinical survival of OC patients. The expression of GFPT2, DGKD, ACACB, and ACSM3 were downregulated in OC tissues, while IDO1, TPMT, and PGP were upregulated in OC tissues than in control. Moreover, DGKD and IDO1 were significantly associated with the human immune system.

Conclusion

The differently expressed metabolism-related genes were identified to be a risk model in the prediction of the prognosis of OC. The identified hub genes related to OC prognosis may play important roles in influencing both human metabolism and the immune system.

Weighted Gene Co-expression Network Analysis Identifies CALD1 as a Biomarker Related to M2 Macrophages Infiltration in Stage III and IV Mismatch Repair-Proficient Colorectal Carcinoma

Immunotherapy has achieved efficacy for advanced colorectal cancer (CRC) patients with a mismatch-repair-deficient (dMMR) subtype. However, little immunotherapy efficacy was observed in patients with the mismatch repair-proficient (pMMR) subtype, and hence, identifying new immune therapeutic targets is imperative for those patients. In this study, transcriptome data of stage III/IV CRC patients were retrieved from the Gene Expression Omnibus database. The CIBERSORT algorithm was used to quantify immune cellular compositions, and the results revealed that M2 macrophage fractions were higher in pMMR patients as compared with those with the dMMR subtype; moreover, pMMR patients with higher M2 macrophage fractions experienced shorter overall survival (OS). Subsequently, weighted gene co-expression network analysis and protein–protein interaction network analysis identified six hub genes related to M2 macrophage infiltrations in pMMR CRC patients: CALD1, COL6A1, COL1A2, TIMP3, DCN, and SPARC. Univariate and multivariate Cox regression analyses then determined CALD1 as the independent prognostic biomarker for OS. CALD1 was upregulated specifically the in CMS4 CRC subtype, and single-sample Gene Set Enrichment Analysis (ssGSEA) revealed that CALD1 was significantly correlated with angiogenesis and TGF-β signaling gene sets enrichment scores in stage III/IV pMMR CRC samples. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm and correlation analysis revealed that CALD1 was significantly associated with multiple immune and stromal components in a tumor microenvironment. In addition, GSEA demonstrated that high expression of CALD1 was significantly correlated with antigen processing and presentation, chemokine signaling, leukocyte transendothelial migration, vascular smooth muscle contraction, cytokine–cytokine receptor interaction, cell adhesion molecules, focal adhesion, MAPK, and TGF-beta signaling pathways. Furthermore, the proliferation, invasion, and migration abilities of cancer cells were suppressed after reducing CALD1 expression in CRC cell lines. Taken together, multiple bioinformatics analyses and cell-level assays demonstrated that CALD1 could serve as a prognostic biomarker and a prospective therapeutic target for stage III/IV pMMR CRCs.

Gene network analysis using SWIM reveals interplay between the transcription factor-encoding genes HMGA1, FOXM1, and MYBL2 in triple-negative breast cancer

Among breast cancer subtypes, triple-negative breast cancer (TNBC) is the most aggressive with the worst prognosis and the highest rates of metastatic disease. To identify TNBC gene signatures, we applied the network-based methodology implemented by the SWIM software to gene expression data of TNBC patients in The Cancer Genome Atlas (TCGA) database. SWIM enables to predict key (switch) genes within the co-expression network, whose perturbations in expression pattern and abundance may contribute to the (patho)biological phenotype. Here, SWIM analysis revealed an interesting interplay between the genes encoding the transcription factors HMGA1, FOXM1, and MYBL2, suggesting a potential cooperation among these three switch genes in TNBC development. The correlative nature of this interplay in TNBC was assessed by in vitro experiments, demonstrating how they may actually modulate the expression of each other.

Overexpressed COL5A1 is correlated with tumor progression, paclitaxel resistance, and tumor-infiltrating immune cells in ovarian cancer

Ovarian cancer (OC) remains the leading cause of cancer-related death among gynecological cancers. The present study examined the role of collagen type V alpha 1 (COL5A1) and the characteristics of COL5A1 as an oncogenic protein in OC. The association of COL5A1 with paclitaxel (PTX)-resistance and stemness in OC was also studied and the multidatabase and big data analyses of the prognostic value, coexpression network, genetic alterations, and tumor-infiltrating immune cells of COL5A1 were elucidated. We found that COL5A1 expression was high in OC cells and tissues. Knockdown of COL5A1 inhibited the proliferation and migration of OC cells. Further study also showed that COL5A1 was overexpressed in PTX-resistant OC cells compared to respective PTX-sensitive cells. Additionally, COL5A1 was more enriched in OC stem cell-like cells. Silencing COL5A1 expression decreased the OC cell resistance to PTX and inhibited the ability of OC-spheroid formation. Survival analysis predicted that the elevated COL5A1 expression was associated with a worse survival outcome and correlated to the tumor stage of OC patients. The estimating relative subsets of RNA transcripts (CIBERSORT) algorithm analysis also unveiled the correlation of several tumor-infiltrating immune cells with the expression of COL5A1. Taken together, our data demonstrate that COL5A1 is a biomarker to predict OC progression and PTX-resistance and represents a promising target for OC treatment.

Optimized combination methods for exploring and verifying disease-resistant transcription factors in melon

A large amount of omics data and number of bioinformatics tools has been produced. However, the methods for further exploring omics data are simple, in particular, to mine key regulatory genes, which are a priority concern in biological systems, and most of the specific functions are still unknown. First, raw data of two genotypes of melon (susceptible and resistant) were obtained by transcriptome analysis. Second, 391 transcription factors (TFs) were identified from the plant transcription factor database and cucurbit genomics database. Then, functional enrichment analysis indicated that these genes were mainly annotated in the process of transcription regulation. Third, 243 and 230 module-specific TFs were screened by weighted gene coexpression network analysis and short time series expression miner, respectively. Several TF genes, such as WRKYs and bHLHs, were regarded as key regulatory genes according to the values of significantly different modules. The coexpression network showed that these TF genes were significant correlated with resistance (R) genes, such as DRP2, RGA3, DRP1 and NB-ARC. Fourth, cis-acting element analysis illustrated that these R genes may bind to WRKY and bHLH. Finally, the expression of WRKY genes was verified by quantitative reverse transcription PCR (RT-qPCR). Phylogenetic analysis was carried out to further confirm that these TFs may play a critical role in Curcurbitaceae disease resistance. This study provides a new optimized combination strategy to explore the functions of TFs in a wide spectrum of biological processes. This strategy may also effectively predict potential relationships in the interactions of essential genes.

Long non‑coding RNA TTN‑AS1 regulates the proliferation, invasion and migration of triple‑negative breast cancer by targeting miR‑211‑5p

Increasing evidence has demonstrated that long non-coding RNAs (lncRNAs) serve important roles in numerous malignancies, including triple-negative breast cancer (TNBC). The lncRNA titin-antisense RNA1 (TTN-AS1) has previously been reported to promote tumorigenesis in various types of cancer. The present study aimed to investigate the potential role of TTN-AS1 in breast cancer and the associated underlying mechanisms. Following prediction by Starbase and confirmation by dual-luciferase reporter assay, TINCR was demonstrated to be a target gene for microRNA (miR)-211-5p. The expression levels of TTN-AS1 and miR-211-5p, which was predicted to be targeted by TTN-AS1, in TNBC tissues and in the breast cancer cell lines MDA-MB-453 and MDA-MB-231 were measured using reverse transcription-quantitative PCR. Following TTN-AS1-knockdown, cell proliferation was measured using a Cell Counting Kit-8 assay and colony formation assay, whereas cell invasion and migration were measured using Transwell and wound healing assays, respectively. Luciferase reporter assay was performed to verify the potential interaction between TTN-AS1 and miR-211-5p. In addition, rescue assays were conducted to investigate the effects of TTN-AS1 and miR-211-5p on TNBC development. The results demonstrated that TTN-AS1 expression was significantly upregulated, whereas that of miR-211-5p was found to be downregulated in TNBC tissues and cell lines compared with the matched adjacent normal tissues and normal breast epithelial cell line MCF-10A, respectively. Furthermore, TTN-AS1-knockdown inhibited the proliferation and invasive and migratory abilities of MDA-MB-453 and MDA-MB-231 cells, which was reversed following co-transfection with the miR-211-5p inhibitor. The results from luciferase reporter assay confirmed that miR-211-5p was a direct target of TTN-AS1, suggesting that TTN-AS1 may bind directly to miR-211-5p to negatively regulate its expression. In conclusion, the findings from the present study demonstrated that TTN-AS1 regulated the proliferation and invasive and migratory abilities of TNBC by targeting miR-211-5p. This study may provide some insights into the regulatory mechanism of TNBC and help the development of novel therapeutic interventions for TNBC.