Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis

Anoikis-related gene signatures in colorectal cancer: implications for cell differentiation, immune infiltration, and prognostic prediction

Colorectal cancer (CRC) is a malignant tumor originating from epithelial cells of the colon or rectum, and its invasion and metastasis could be regulated by anoikis. However, the key genes and pathways regulating anoikis in CRC are still unclear and require further research. The single cell transcriptome dataset GSE221575 of GEO database was downloaded and applied to cell subpopulation type identification, intercellular communication, pseudo time cell trajectory analysis, and receptor ligand expression analysis of CRC. Meanwhile, the RNA transcriptome dataset of TCGA, the GSE39582, GSE17536, and GSE17537 datasets of GEO were downloaded and merged into one bulk transcriptome dataset. The differentially expressed genes (DEGs) related to anoikis were extracted from these data sets, and key marker genes were obtained after feature selection. A clinical prognosis prediction model was constructed based on the marker genes and the predictive effect was analyzed. Subsequently, gene pathway analysis, immune infiltration analysis, immunosuppressive point analysis, drug sensitivity analysis, and immunotherapy efficacy based on the key marker genes were conducted for the model. In this study, we used single cell datasets to determine the anoikis activity of cells and analyzed the DEGs of cells based on the score to identify the genes involved in anoikis and extracted DEGs related to the disease from the transcriptome dataset. After dimensionality reduction selection, 7 marker genes were obtained, including TIMP1, VEGFA, MYC, MSLN, EPHA2, ABHD2, and CD24. The prognostic risk model scoring system built by these 7 genes, along with patient clinical data (age, tumor stage, grade), were incorporated to create a nomogram, which predicted the 1-, 3-, and 5-years survival of CRC with accuracy of 0.818, 0.821, and 0.824. By using the scoring system, the CRC samples were divided into high/low anoikis-related prognosis risk groups, there are significant differences in immune infiltration, distribution of immune checkpoints, sensitivity to chemotherapy drugs, and efficacy of immunotherapy between these two risk groups. Anoikis genes participate in the differentiation of colorectal cancer tumor cells, promote tumor development, and could predict the prognosis of colorectal cancer.

A neutrophil extracellular trap-related risk score predicts prognosis and characterizes the tumor microenvironment in multiple myeloma

Multiple myeloma (MM) is a distinguished hematologic malignancy, with existing studies elucidating its interaction with neutrophil extracellular traps (NETs), which may potentially facilitate tumor growth. However, systematic investigations into the role of NETs in MM remain limited. Utilizing the single-cell dataset GSE223060, we discerned active NET cell subgroups, namely neutrophils, monocytes, and macrophages. A transcriptional trajectory was subsequently constructed to comprehend the progression of MM. Following this, an analysis of cellular communication in MM was conducted with a particular emphasis on neutrophils, revealing an augmentation in interactions albeit with diminished strength, alongside abnormal communication links between neutrophils and NK cells within MM samples. Through the intersection of differentially expressed genes (DEGs) between NET active/inactive cells and MM versus healthy samples, a total of 316 genes were identified. This led to the development of a 13-gene risk model for prognostic prediction based on overall survival, utilizing transcriptomics dataset GSE136337. The high-risk group manifested altered immune infiltration and heightened sensitivity to chemotherapy. A constructed nomogram for predicting survival probabilities demonstrated encouraging AUCs for 1, 3, and 5-year survival predictions. Collectively, our findings unveil a novel NET-related prognostic signature for MM, thereby providing a potential avenue for therapeutic exploration.

Targeted Therapies and Drug Resistance in Advanced Breast Cancer, Alternative Strategies and the Way beyond

"Targeted therapy" or "precision medicine" is a therapeutic strategy launched over two decades ago. It relies on drugs that inhibit key molecular mechanisms/pathways or genetic/epigenetic alterations that promote different cancer hallmarks. Many clinical trials, sponsored by multinational drug companies, have been carried out. During this time, research has increasingly uncovered the complexity of advanced breast cancer disease. Despite high expectations, patients have seen limited benefits from these clinical trials. Commonly, only a minority of trials are successful, and the few approved drugs are costly. The spread of this expensive therapeutic strategy has constrained the resources available for alternative research. Meanwhile, due to the high cost/benefit ratio, other therapeutic strategies have been proposed by researchers over time, though they are often not pursued due to a focus on precision medicine. Notable among these are drug repurposing and counteracting micrometastatic disease. The former provides an obvious answer to expensive targeted therapies, while the latter represents a new field to which efforts have recently been devoted, offering a "way beyond" the current research.

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

BACKGROUND

There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.

METHODS

Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.

RESULTS

The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.

CONCLUSION

This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

Identification of AP002498.1 and LINC01871 as prognostic biomarkers and therapeutic targets for distant metastasis of colorectal adenocarcinoma

BACKGROUND

Increasing evidence suggests that lncRNA (Long non-coding RNA, lncRNA)-mediated ceRNA (competing endogenous RNA, ceRNA) networks are involved in the occurrence and progression of colorectal cancer (CRC). However, the roles of the lncRNA-miRNA-mRNA ceRNA network in distant metastasis of CRC are still unclear.

METHODS

In this study, we constructed a specific ceRNA network to identify potential biomarkers and therapeutic targets for distant metastasis of CRC. Specifically, RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to screen for differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) related to metastasis. After validation and selection by qRT-PCR and univariate and multivariate analysis of the metastasis- and prognosis-related lncRNAs, the regulated microRNAs (miRNAs) and coexpressed mRNAs were used to construct a ceRNA network for distant metastasis of CRC.

RESULTS

Two key distant metastasis-related DElncRNAs, AP002498.1 and LINC01871, were identified by univariate and multivariate analysis in combination with analyses of clinical data and expression levels. Furthermore, lncRNA-associated ceRNA subnetworks were constructed from the predicted miRNAs and 13 coexpressed DEmRNAs (SERPINA1, ITLN1, REG4, L1TD1, IGFALS, MUC5B, CIITA, CXCL9, CXCL10, GBP4, GNLY, IDO1, and NOS2). The AP002498.1- and LINC01871-associated ceRNA subnetworks regulated the expression of the target genes SERPINA1 and MUC5B and GNLY, respectively, through the associated miRNAs.

CONCLUSION

The DElncRNA AP002498.1 and the LINC01871/miR-4644 and miR-185-5p/GNLY axes were identified as being closely associated with distant metastasis and could represent independent prognostic biomarkers or therapeutic targets in colorectal adenocarcinoma.

MED1 induces M2 polarization of tumor-associated macrophages to aggravate breast cancer

BACKGROUND

Breast cancer is a common malignant tumor in female, and its 5-year survival rate remains low. The correlation between mediator subunit 1 (MED1) gene and macrophage phenotypic transformation may be a key factor affecting the therapeutic effect on cancer.

OBJECTIVE

The present study intended to explore the role of MED1 in macrophage polarization and its further influence on the malignant behaviors of breast cancer.

METHODS

Bioinformatics analysis was carried out to predict the expression pattern of MED1 in breast cancer. Flow cytometry was conducted to detect the effect of MED1 overexpression or silencing on macrophage polarization. ELISA was applied to analyze the effect of abnormal MED1 expression on cytokine secretion of macrophages. CCK-8, colony formation, Transwell and scratch healing assays were applied to investigate the effects of macrophage conditioned medium on the malignant behaviors of breast cancer cells.

RESULTS

MED1 expression was prominently increased in M2 macrophages, and overexpression of MED1 significantly increased M2 polarization of tumor-associated macrophages (TAMs) and IL-10 cytokine level. Meanwhile, M2 macrophages with MED1 overexpression could significantly promote the malignant behaviors of breast cancer cells. Dasatinib rescue experiment further confirmed that MED1-induced M2 macrophage polarization could facilitate the malignant progression of breast cancer cells.

CONCLUSION

In summary, MED1 could induce M2 macrophage polarization and thus regulate the malignant behaviors of breast cancer cells.

The Diagnostic and Prognostic Value of Neurotransmitter Receptor-Related Genes in Colon Adenocarcinoma

Colorectal cancer (CRC) is a malignant tumor with high morbidity and mortality in the world. This study aimed to find receptor-related genes (NRGs) with diagnostic and prognostic value in colon adenocarcinoma (COAD). The Cancer Genome Atlas (TCGA) and the Human Protein Atlas database databases were applied to find differential expression NRGs between COAD and normal colonic tissues. Subsequently, Cox regression analysis and minimum absolute contraction and selection operator algorithm were used to construct a prognosis nomogram based on TCGA and Gene Expression Omnibus databases. Expression levels of 35 NRGs were significant differences in COAD and normal colonic tissues. ROC curves showed that 24 NRGs had high diagnostic accuracy (AUC > 0.850) in COAD. Risk score was constructed based on 10 NRGs for the first time. Cox regression analysis revealed risk score was an independent risk factor and a higher risk score predicts a later TNM stage. Finally, a prognostic nomogram containing risk score and clinical features was established. Calibration curves and C-index suggested the powerful predictable value of the model. This study identified the NRGs with diagnostic value and prognostic value, providing a direction for treatment of COAD patients.

A prognostic model related to necrotizing apoptosis of breast cancer based on biorthogonal constrained depth semi-supervised nonnegative matrix decomposition and single-cell sequencing analysis

Breast cancer (BC) is one of the most common malignant tumours in women, and its prognosis is poor. The prognosis of BC patients can be improved by immunotherapy. However, due to the heterogeneity of BC, the identification of new biomarkers is urgently needed to improve the prognosis of BC patients. Necrotic apoptosis has been shown to play an essential role in many cancers. First, this study proposed a novel clustering algorithm called biorthogonal constrained depth semisupervised nonnegative matrix factorization (DO-DSNMF). The DO-DSNMF algorithm added multilayer nonlinear transformation to the coefficient matrix obtained after decomposition, which was used to mine the nonlinear relationship between samples. In addition, we also added orthogonal constraints on the basis matrix and coefficient matrix to reduce the influence of redundant features and samples on the results. We applied the DO-DSNMF algorithm and analysed the differences in survival and immunity between the subtypes. Then, we used prognosis analysis to construct the prognosis model. Finally, we analysed single cells using single-cell sequencing (scRNA-seq) data from the GSE75688 dataset in the GEO database. We identified two BC subtypes based on the BC transcriptome data in the TCGA database. Immune infiltration analysis showed that the necrotizing apoptosis-related genes of BC were related to various immune cells and immune functions. Necrotizing apoptosis was found to play a role in BC progression and immunity. The role of prognosis-related NRGs in BC was also verified by cell experiments. This study proposed a novel clustering algorithm to analyse BC subtypes and constructed an NRG prognostic model for BC. The prognosis and immune landscape of BC patients were evaluated by this model. The cell experiment supported its role in BC, which provides a potential therapeutic target for the treatment of BC.

Identification of a glycolysis-related miRNA Signature for Predicting Breast cancer Survival

Breast cancer (BC) is a common type of cancer and has a poor prognosis. In this study, we collected the mRNA and miRNA expression profiles of BC patients were obtained from The Cancer Genome Atlas (TCGA) to explore a novel prognostic strategy for BC patients using bioinformatics tools. We found that six glycolysis-related miRNAs (GRmiRs, including hsa-mir-1247, hsa-mir148b, hsa-mir-133a-2, has-mir-1307, hsa-mir-195 and hsa-mir-1258) were correlated with prognosis of BC samples. The risk score model was established based on 6 prognosis-associated GRmiRs. The outcome of high risk group was significantly poorer. Cox regression analysis showed that risk score was an independent prognostic factor. Differentially expressed genes identified between high and low risk groups were mainly enriched in inflammation and immune-related signaling pathways. The proportion of infiltration of 12 kinds of immune cells in high and low risk groups were significantly different. Risk score was closely associated with many immune indexes. Multiple DEGRGs and miRNAs were associated with drugs. In conclusion, glycolysis-related miRNA signature effectively predicts BC prognosis.

Immune gene signatures as prognostic criteria for cancer patients

Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

A Novel Prognostic Signature of comprising Nine NK Cell signatures Based on Both Bulk RNA Sequencing and Single-Cell RNA Sequencing for Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) has limited prognostic prediction due to its heterogeneity. Understanding the role of natural killer (NK) cells in HCC is vital for prognosis and immunotherapy guidance. We aimed to identify NK cell marker genes through scRNA-seq and develop a prognostic signature for HCC. Methods: We analyzed scRNA-seq data (GSE149614) from 10 patients and bulk RNA-seq data from 786 patients with clinicopathological information. NK cell marker genes were identified using clustering and marker finding functions. A predictive risk signature was constructed using LASSO-COX algorithm. Functional annotations and immune cell infiltration analysis were performed, and the nomogram's performance was evaluated. Results: We identified 79 NK cell marker genes associated with NK cell-mediated cytotoxicity, apoptosis, and immune response. The multigene signature significantly correlated with overall survival (OS) in TCGA-LIHC cohort and was validated in other cohorts. Low-risk patients exhibited higher immune cell infiltration, including CD8+ T cells. The risk signature was an independent prognostic factor for OS (HR > 1, p < 0.001). The nomogram combining the risk signature and clinical predictors demonstrated robust prognostic ability. Conclusion: We developed a nine-gene signature prognostic model based on NK cell marker genes to accurately assess the prognostic risk of HCC. This model can be a valuable tool for personalized evaluation post-surgery. Our study underscores the potential of NK cells in HCC prognosis and highlights the importance of scRNA-seq analysis in identifying prognostic markers.

Natural killer cell-related prognostic risk model predicts prognosis and treatment outcomes in triple-negative breast cancer

Background

Natural killer (NK) cells are crucial to the emergence, identification, and prognosis of cancers. The roles of NK cell-related genes in the tumor immune microenvironment (TIME) and immunotherapy treatment are unclear. Triple-negative breast cancer (TNBC) is a highly aggressive malignant tumor. Hence, this study was conducted to develop a reliable risk model related to NK cells and provide a novel system for predicting the prognosis of TNBC.

Methods

NK cell-related genes were collected from previous studies. Based on TCGA and GEO database, univariate and LASSO cox regression analysis were used to establish the NK cell-related gene signature. The patients with TNBC were separated to high-risk and low-risk groups. After that, survival analysis was conducted and the responses to immunotherapies were evaluated on the basis of the signature. Moreover, the drug sensitivity of some traditional chemotherapeutic drugs was assessed by using the "oncoPredict" R package. In addition, the expression levels of the genes involved in the signature were validated by using qRT-PCR in TNBC cell lines.

Results

The patients with TNBC were divided into high- and low-risk groups according to the median risk score of the 5-NK cell-related gene signature. The low-risk group was associated with a better clinical outcome. Besides, the differentially expressed genes between the different risk groups were enriched in the biological activities associated with immunity. The tumor immune cells were found to be highly infiltrated in the low-risk groups. In accordance with the TIDE score and immune checkpoint-related gene expression analysis, TNBC patients in the low-risk groups were suggested to have better responses to immunotherapies. Eventually, some classical anti-tumor drugs were shown to be less effective in high-risk groups than in low-risk groups.

Conclusion

The 5-NK cell-related gene signature exhibit outstanding predictive performance and provide fresh viewpoints for evaluating the success of immunotherapy. It will provide new insights to achieve precision and integrated treatment for TNBC in the future.

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

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

A novel PD-1/PD-L1 pathway molecular typing-related signature for predicting prognosis and the tumor microenvironment in breast cancer

BACKGROUND

Currently, the development of breast cancer immunotherapy based on the PD-1/PD-L1 pathway is relatively slow, and the specific mechanism affecting the immunotherapy efficacy in breast cancer is still unclear.

METHODS

Weighted correlation network analysis (WGCNA) and the negative matrix factorization (NMF) were used to distinguish subtypes related to the PD-1/PD-L1 pathway in breast cancer. Then univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were used to construct the prognostic signature. A nomogram was established based on the signature. The relationship between the signature gene IFNG and breast cancer tumor microenvironment was analyzed.

RESULTS

Four PD-1/PD-L1 pathway-related subtypes were distinguished. A prognostic signature related to PD-1/PD-L1 pathway typing was constructed to evaluate breast cancer's clinical characteristics and tumor microenvironment. The nomogram based on the RiskScore could be used to accurately predict breast cancer patients' 1-year, 3-year, and 5-year survival probability. The expression of IFNG was positively correlated with CD8+ T cell infiltration in the breast cancer tumor microenvironment.

CONCLUSION

A prognostic signature is constructed based on the PD-1/PD-L1 pathway typing in breast cancer, which can guide the precise treatment of breast cancer. The signature gene IFNG is positively related to CD8+ T cell infiltration in breast cancer.

The PTEN-associated immune prognostic signature reveals the landscape of the tumor microenvironment in glioblastoma

Glioblastoma (GBM) is a common brain tumor with a complex and diverse tumor microenvironment (TME). As PTEN mutation is the most common mutation in GBM, we aimed to investigate how PTEN mutation regulates the immune response in GBM TME and thus affects the prognosis of GBM patients. In this study, we conducted a comprehensive analysis of multiple levels of data, including whole-exome sequencing (WES), transcriptome RNA sequencing, patient survival and immune signatures, to study the relationship between PTEN mutation and TME in GBM. We developed an immune-related prognostic signature (IPS) based on the PTEN-associated immune-related genes (IRGs), and the IPS exhibited a powerful prognosis prediction capacity in different GBM cohorts. A scoring nomogram based on the IPS was also established for clinical application. In addition, the correlations of the IPS with tumor immune cell infiltration and immune checkpoints were systematically analyzed. This study illustrates the influence of PTEN mutation on the immune microenvironment of GBM. Our IPS, which is sensitive to PTEN mutation status, can enhance the prognosis prediction ability for GBM patients and provides potential targets for immunotherapy.

Functional impact of multi-omic interactions in breast cancer subtypes

Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.

Single-Cell RNA Sequencing Reveals the Role of Epithelial Cell Marker Genes in Predicting the Prognosis of Colorectal Cancer Patients

Single-cell RNA sequencing (scRNA-seq) is increasingly used in studies on gastrointestinal cancers. This study investigated the prognostic value of epithelial cell-associated biomarkers in colorectal cancer (CRC) using scRNA-seq data. We downloaded and analysed scRNA-seq data from four CRC samples from the Gene Expression Omnibus (GEO), and we identified marker genes of malignant epithelial cells (MECs) using CRC transcriptome and clinical data downloaded from The Cancer Genome Atlas (TCGA) and GEO as training and validation cohorts, respectively. In the TCGA training cohort, weighted gene correlation network analysis, univariate Cox proportional hazard model (Cox) analysis, and least absolute shrinkage and selection operator regression analysis were performed on the marker genes of MEC subsets to identify a signature of nine prognostic MEC-related genes (MECRGs) and calculate a risk score based on the signature. CRC patients were divided into high- and low-risk groups according to the median risk score. We found that the MECRG risk score was significantly correlated with the clinical features and overall survival of CRC patients, and that CRC patients in the high-risk group showed a significantly shorter survival time. The univariate and multivariate Cox regression analyses showed that the MECRG risk score can serve as an independent prognostic factor for CRC patients. Gene set enrichment analysis revealed that the MECRG signature genes are involved in fatty acid metabolism, p53 signalling, and other pathways. To increase the clinical application value, we constructed a MECRG nomogram by combining the MECRG risk score with other independent prognostic factors. The validity of the nomogram is based on receiver operating characteristics and calibration curves. The MECRG signature and nomogram models were well validated in the GEO dataset. In conclusion, we established an epithelial cell marker gene-based risk assessment model based on scRNA-seq analysis of CRC samples for predicting the prognosis of CRC patients.

New insights from the single-cell level: Tumor associated macrophages heterogeneity and personalized therapy

Tumor-associated macrophages (TAMs) are important immune cells in the tumor microenvironment, and their invasion in tumors is closely related to poor prognosis. Although TAMs are recognized as therapeutic targets, their heterogeneity makes studying tumor mechanism and developing drugs targeting TAMs difficult. The study of TAMs heterogeneity can be used to analyze the mechanism of tumor progression and drug resistance, and may provide possible treatment strategies for cancer patients. Single-cell RNA sequencing (scRNA-seq) can reveal the RNA expression profile for each TAM to distinguish heterogeneity, thereby providing a more efficient detection method and more accurate information for TAM-related studies. In this review, by summarizing the research progress in macrophage heterogeneity and other aspects of scRNA-seq over the past five years, we introduced the development of scRNA-seq technology and its application status in solid tumors, analyzed the advantages and selections of scRNA-seq in TAMs, and summarized the detailed specific research fields. To explore the mechanism of tumor progression and drug intervention from single cell level will provide new perspective for personalized treatment strategies targeting macrophages.

Identification and Validation of a Novel Signature Based on NK Cell Marker Genes to Predict Prognosis and Immunotherapy Response in Lung Adenocarcinoma by Integrated Analysis of Single-Cell and Bulk RNA-Sequencing

Natural killer (NK) cells, the effectors of the innate immune system, have a remarkable influence on cancer prognosis and immunotherapy. In this study, a total of 1,816 samples from nine independent cohorts in public datasets were enrolled. We first conducted a comprehensive analysis of single-cell RNA-sequencing data of lung adenocarcinoma (LUAD) from the Gene Expression Omnibus (GEO) database and determined 189 NK cell marker genes. Subsequently, we developed a seven-gene prognostic signature based on NK cell marker genes in the TCGA LUAD cohort, which stratified patients into high-risk and low-risk groups. The predictive power of the signature was well verified in different clinical subgroups and GEO cohorts. With a multivariate analysis, the signature was identified as an independent prognostic factor. Low-risk patients had higher immune cell infiltration states, especially CD8⁺ T cells and follicular helper T cells. There existed a negative association between inflammatory activities and risk score, and the richness and diversity of the T-cell receptor (TCR) repertoire was higher in the low-risk groups. Importantly, analysis of an independent immunotherapy cohort (IMvigor210) revealed that low-risk patients had better immunotherapy responses and prognosis than high-risk patients. Collectively, our study developed a novel signature based on NK cell marker genes, which had a potent capability to predict the prognosis and immunotherapy response of LUAD patients.

A Novel Five-Gene Signature Related to Clinical Outcome and Immune Microenvironment in Breast Cancer

Breast cancer (BC) is the most frequent cancer in women and the main cause of cancer-related deaths in the globe, according to the World Health Organization. The need for biomarkers that can help predict survival or guide treatment decisions in BC patients is critical in order to provide each patient with an individualized treatment plan due to the wide range of prognoses and therapeutic responses. A reliable prognostic model is essential for determining the best course of treatment for patients. Patients’ clinical and pathological data, as well as their mRNA expression levels at level 3, were gleaned from the TCGA databases. Differentially expressed genes (DEGs) between BC and non-tumor specimens were identified. Tumor immunity analyses have been utilized in order to decipher molecular pathways and their relationship to the immune system. The expressions of KIF4A in BC cells were determined by RT-PCR. To evaluate the involvement of KIF4A in BC cell proliferation, CCK-8 tests were used. In this study, utilizing FC > 4 and p < 0.05, we identified 140 upregulated genes and 513 down-regulated genes. A five-gene signature comprising SFRP1, SAA1, RBP4, KIF4A and COL11A1 was developed for the prediction of overall survivals of BC. Overall survival was distinctly worse for patients in the high-risk group than those in the low-risk group. Cancerous and aggressiveness-related pathways and decreased B cell, T cell CD4⁺, T cell CD8⁺, Neutrophil and Myeloid dendritic cells levels were seen in the high-risk group. In addition, we found that KIF4A was highly expressed in BC and its silence resulted in the suppression of the proliferation of BC cells. Taken together, as a possible prognostic factor for BC, the five-gene profile created and verified in this investigation could guide the immunotherapy selection.