Cancer-associated hypersialylation is believed to be related to the metastatic cell phenotype and the suppression of sialyltransferases (SiaTs) has been suggested to be a potent preventive strategy against metastasis. The present research discovered SiaTs-related genes for cervical cancer (CC).

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were applied to obtain the relevant samples. Mutation dataset were processed using mutect2 software. The gene modules were obtained via weighted gene co-expression network analysis (WGCNA), and the enrichment analysis on the genes within the modules was implemented. Cox regression analysis and "glmnet" R package were applied to establish the relevant risk model. "MCPcounter" R package, ESTIMATE algorithm and TIMER online tools were used to depict the tumor immune microenvironment in CC. The mutation landscape was additionally plotted, and the response to immunotherapy in different cohorts were compared. Further reverse-transcription quantitative PCR and Transwell assays were performed to verify the expression and potential function of the screened key genes.

Mutation of 14 SiaTs was seen in CC. Subsequently, WGCNA-based identification of SiaTs-related gene modules was significantly enriched in metabolism-related pathways. The established RiskScore model manifested excellent prognostic classification efficiency. A poorer prognosis and occurrence of both immune evasion and reduced immunoreactivity may be seen in high-risk patients yet relatively higher immune cell scores were noticeable in low-risk patients. Angiogenesis and MYC target V2 may be the differentially activated pathways in high-risk patients, while those in low-risk patients were KRAS Signaling DN and Interferon alpha response. In addition, most immune checkpoint-correlated genes were identified to express higher in low-risk patients, while higher sensitivities to chemotherapy drugs was discovered in high-risk patients. Cellular assays have revealed that KCNK15, LIF, TCN2, SERPINF2, and CXCL3 were highly expressed yet PIH1D2, DTX1 and GCNT2 were low-expressed in Hela cells and that silencing CXCL3 diminished the number of migrated and invaded Hela cells.

In this study, we systematically constructed and validated a risk scoring model based on SiaTs-related genes, which can effectively predict the prognosis and potential response to immunotherapy and chemotherapy in CC patients. This provides a new molecular basis and clinical reference for achieving individualized treatment.

The tumor microenvironment (TME) is the combination of cells and factors that promotes tumor progression, and cancer-associated fibroblasts (CAFs) are a key component within TME. CAF originates from various stromal cells and is activated by factors such as transforming growth factor-beta (TGF-β) secreted by tumor cells, favoring chemoresistance and metastasis. Recent publications have underlined plasticity and heterogeneity and their strong contribution to the reactive stroma within the TME. Our study aimed to replicate the TME's structure by creating a 3D in vitro model of ovarian cancer (OC). By incorporating diverse tumor and stromal cells, we simulated a physiologically relevant environment for studying CAF-like cell behavior within tumor spheroids in a context-dependent manner. CAF-like cells were generated by exposing human dermal fibroblasts to OC cell line conditioned media in the presence or absence of TGF-β. Herein, we found that different stimuli induce the generation of heterogeneous populations of CAF-like cells. Notably, we observed the ability of CAF-like cells to shape the intratumoral architecture and to contribute to functional changes in tumor cell behavior. This study highlights the importance of precise assessment of CAF for potential therapeutic interventions and further provides a reliable model for investigating novel therapeutic targets in OC.

Liver metastasis is the most common site of metastasis in colorectal cancer, and the prognosis of colorectal cancer patients with liver metastasis is extremely poor. Revealing the key genes of CLM and implementing targeted interventions is of great significance for colorectal cancer patients. By using the weighted gene co-expression network analysis (WGCNA) algorithm, key gene modules related to metastasis in colorectal cancer were identified. Subsequently, immune-regulating and prognostic-influencing key gene sets were identified from these modules to construct a prognostic model related to colorectal cancer metastasis. Genetic background differences underlying this model were analyzed using colorectal cancer methylation and mutation data, followed by Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) analysis of the relevant biological processes associated with the model. The value of predicting tumor drug response through the model was assessed using drug half maximal inhibitory concentration (IC50) data from colorectal cancer cell lines. Subsequently, utilizing single-cell sequencing data about liver metastasis, the colorectal cancer immune microenvironment reflected in the predictive model was analyzed, and a key gene set of the model was identified. Lastly, experimental validation was conducted to investigate the regulatory effects of photodynamic therapy (PDT) on the key genes of the model, and the cytotoxic effect of PDT on colorectal cancer was confirmed. An immune-related gene prognostic model regulating CLM was constructed, consisting of HSPA1A, ULBP2, RBP7, OXT, SLC11A1, INHBB, and ICOS. This model can predict the clinical response of colorectal cancer patients to Oxaliplatin, Cisplatin, Irinotecan, and 5-Fluorouracil. Single-cell sequencing results demonstrate that the model is associated with an immunosuppressive microenvironment in CLM. The higher the model's riskscore, the weaker the MHC-I, MHC-II, and various tumor immune signaling pathway networks in the colorectal cancer microenvironment. Causal analysis reveals that SLC11A1, ICOS, and HSPA1A play key roles in this model. PDT can kill colorectal cancer cells, inhibit colorectal cancer cell metastasis, significantly influence the expression of genes such as SLC11A1, ICOS, and HSPA1A in these processes, and suppress the infiltration of macrophages in the colorectal microenvironment, inhibiting the immune escape process of PD-1/PD-L1. A prognostic model based on immunity regulated by PDT has been established for assessing the prognosis of CLM patients, as well as clinical responses to chemotherapy drugs and immunotherapy.

Cellular senescence is a hallmark for cancers, particularly in lung adenocarcinoma (LUAD). This study developed a risk model using senescence signature genes for LUAD patients. Based on the RNA-seq, clinical information and mutation data of LUAD patients collected from the TCGA and GEO database, we obtained 102 endotheliocyte senescence-related genes. The "ConsensusClusterPlus" R package was employed for unsupervised cluster analysis, and the "limma" was used for the differentially expressed gene (DEG) analysis. A prognosis model was created by univariate and multivariate Cox regression analysis combined with Lasso regression utilizing the "survival" and "glmnet" packages. KM survival and receiver operator characteristic curve analyses were conducted applying the "survival" and "timeROC" packages. "MCPcounter" package was used for immune infiltration analysis. Immunotherapy response analysis was performed based on the IMvigor210 and GSE78220 cohort, and drug sensitivity was predicted by the "pRRophetic" package. Cell invasion and migration were tested by carrying out Transwell and wound healing assays. According to the results, a total of 32 genes related to endotheliocyte senescence were screened to assign patients into C1 and C2 subtypes. The C2 subtype showed a significantly worse prognosis and an overall higher somatic mutation frequency, which was associated with increased activation of cancer pathways, including Myc_targets2 and angiogenesis. Then, based on the DEGs between the two subtypes, we constructed a five-gene RiskScore model with a strong classification effectiveness for short- and long-term OS prediction. High- and low-risk groups of LUAD patients were classified by the RiskScore. High-risk patients, characterized by lower immune infiltration, had poorer outcomes in both training and validation datasets. The RiskScore was associated with the immunotherapy response in LUAD. Finally, we found that potential drugs such as Cisplatin can benefit high-risk LUAD patients. In-vitro experiments demonstrated that silencing of Angiopoietin-like 4 (ANGPTL4), Gap Junction Protein Beta 3 (GJB3), Family with sequence similarity 83-member A (FAM83A), and Anillin (ANLN) reduced the number of invasive cells and the wound healing rate, while silencing of solute carrier family 34 member 2 (SLC34A2) had the opposite effect. This study, collectively speaking, developed a prognosis model with senescence signature genes to facilitate the diagnosis and treatment of LUAD.

Cancer-associated fibroblasts (CAFs) are critical components of the tumor microenvironment (TME), playing significant roles in regulating cancer progression. However, the underlying mechanism of CAFs activation remains elusive. In this study, we aim to investigates the mechanisms by which CAFs promote the conversion of normal fibroblasts (NFs) to CAFs in lung cancer, with a focus on the role of p53 mutations and the CXCL12/STAT3 signaling axis. We found that CAFs significantly induced NFs to acquire CAFs properties (called CEFs), including upregulation of α-SMA and Vimentin, enhanced proliferation and migration, and increased ability to promote lung cancer cell migration. In vivo, CEFs accelerated A549 xenograft growth and induced spontaneous lung metastasis. CXCL12 was identified as a key factor in NFs-to-CEFs conversion, with its expression positively correlated with CAFs markers in lung cancer. Further investigation confirmed that CXCL12 is sufficient to reprogram NFs into CAFs through the STAT3 pathway. Notably, inhibiting CXCL12 signaling and the STAT3 pathway reduced the conversion of NFs to CAFs, thereby hindering lung cancer progression progression both in vitro and in vivo. Our study reveals CAFs could promote the conversion of NFs to CAFs-like cells through the CXCL12/STAT3 axis, enhancing tumor growth and metastasis in lung cancer. Therefore, inhibition of the CXCL12/STAT3 axis is a promising strategy for the treatment of lung cancers and other CXCL12-dependent malignancies.

Metabolism and stemness-related genes (msRGs) are critical in the development and progression of lung adenocarcinoma (LUAD). Nevertheless, reliable prognostic risk signatures derived from msRGs have yet to be established.

In this study, we downloaded and analyzed RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We employed univariate and multivariate Cox regression analyses, along with least absolute shrinkage and selection operator (LASSO) regression analysis, to identify msRGs that are linked to the prognosis of LUAD and to develop the prognostic risk signature. The prognostic value was evaluated using Kaplan-Meier analysis and log-rank tests. We generated receiver operating characteristic (ROC) curves to evaluate the predictive capability of the prognostic signature. To estimate the relative proportions of infiltrating immune cells, we utilized the CIBERSORT algorithm and the MCPCOUNTER method. The prediction of the half-maximal inhibitory concentration (IC50) for commonly used chemotherapy drugs was conducted through ridge regression employing the "pRRophetic" R package. The validation of our analytical findings was performed through both in vivo and in vitro studies.

A novel five-gene prognostic risk signature consisting of S100P, GPX2, PRC1, ARNTL2, and RGS20 was developed based on the msRGs. A risk score derived from this gene signature was utilized to stratify LUAD patients into high- and low-risk groups, with the former exhibiting significantly poorer overall survival (OS). A nomogram was constructed incorporating the risk score and other clinical characteristics, showcasing strong capabilities in estimating the OS rates for LUAD patients. Furthermore, we observed notable differences in the infiltration of various immune cell subtypes, as well as in responses to immunotherapy and chemotherapy, between the low-risk and high-risk groups. Results from gene set enrichment analysis (GSEA) and in vitro studies indicated that the prognostic signature gene ARNTL2 influenced the prognosis of LUAD patients, primarily through the activation of the PI3K/AKT/mTOR signaling pathway.

Utilizing this gene signature for risk stratification could help with clinical treatment management and improve the prognosis of LUAD patients.

Gastric cancer (GC) remains a major global health challenge, characterized by a complex tumor microenvironment (TME) that significantly influences disease progression and therapeutic outcomes. This study focuses on TREM2+ lipid-associated macrophages (LAM) and cancer-associated fibroblasts (CAFs) in modulating the GC microenvironment. Utilizing advanced single-cell RNA sequencing and bulk RNA analyses, we elucidated the interactive mechanisms through which CAFs enhance the immunosuppressive capabilities of TREM2+ LAMs via the CXCL12-CXCR4 signaling axis. Our findings reveal that this interaction facilitates tumor proliferation and inhibits apoptotic processes in GC cells. In vitro experiments confirmed the modulation of this pathway significantly affects tumor cell viability and invasiveness, underscoring the critical roles of these cellular interactions in promoting GC progression. These insights present TREM2+ LAMs and CAFs as potential therapeutic targets, offering new avenues for improving outcomes in GC treatment.

Breast cancer (BRCA) is a common malignant tumor, and its immune microenvironment plays a crucial role in disease progression. In this research, we utilized single-cell RNA sequencing and bulk RNA sequencing technologies, combined with in vivo and in vitro experiments, to thoroughly investigate the immunological functions and mechanisms of FOLR2+ macrophages in BRCA. Our findings demonstrate a significant enhancement in the interaction between FOLR2+ macrophages and CD8+ T cells within the tumor tissues of BRCA patients. FOLR2 is closely associated with T cell infiltration in the tumor microenvironment of BRCA patients, particularly with CD8+ T cells. By secreting CXCL9 and engaging with CXCR3, FOLR2+ macrophages can activate the functionality of CD8+ T cells, thereby promoting cancer cell apoptosis. Further animal experiments confirm that FOLR2+ macrophages activate CD8+ T cells through the CXCL9-CXCR3 axis, exhibiting an antitumor immunity effect in BRCA. FOLR2+ macrophages play a crucial role in antitumor immunity in BRCA through the CXCL9-CXCR3 axis.

Colorectal cancer (CRC) exhibits a complex tumor microenvironment with significant cellular heterogeneity, particularly involving cancer-associated fibroblasts that influence tumor behavior and metastasis. This study integrated single-cell RNA sequencing and spatial transcriptomics to dissect fibroblast heterogeneity in CRC. Data processing employed Seurat for quality control, principal component analysis for dimensionality reduction, and t-Distributed Stochastic Neighbor Embedding for visualization. Differentially expressed genes were identified using DESeq2. Immune infiltration was assessed via Single-Sample Gene Set Enrichment Analysis, CIBERSORT, and xCell algorithms. Prognostic genes were identified through univariate Cox regression, followed by consensus clustering and survival analysis. Metabolic pathways were explored using scMetabolism. Experimental validation involved CCK8, scratch, and Transwell assays to evaluate the roles of key genes BGN and CERCAM in CRC cell proliferation and metastasis. Machine learning-driven analysis identified four fibroblast-associated genes (TRIP6, TIMP1, BGN, and CERCAM) demonstrating significant prognostic relevance in CRC. Consensus clustering based on these biomarkers stratified CRC patients into three distinct molecular subtypes (Clusters A-C). Notably, Cluster C exhibited the most unfavorable clinical outcomes coupled with marked upregulation of all four fibroblast-related genes. Comprehensive immune profiling revealed paradoxical features in Cluster C: heightened global immune activation (characterized by substantial leukocyte infiltration) coexisted with specific immunosuppressive elements, including significant enrichment of pro-tumorigenic M0 macrophages, depletion of anti-tumor plasma cells, and resting memory CD4+ T cells, along with coordinated upregulation of multiple immune checkpoint molecules. Computational prediction using the TIDE platform suggested enhanced immunotherapy responsiveness in Cluster C patients. Functional validation demonstrated that knockdown of BGN or CERCAM significantly impaired malignant phenotypes, reducing proliferative capacity, migration potential, and invasive ability. Fibroblasts demonstrate significant heterogeneity within the CRC immune microenvironment, impacting prognosis and therapeutic responses. Key genes BGN and CERCAM emerge as potential immunotherapeutic targets, offering new strategies for precision treatment of CRC.

Cancer-associated fibroblasts (CAFs) and immune cells, the key components of the tumor microenvironment (TME), play critical roles in oncogenesis. Despite the recognized function of fibroblast activation protein-α (FAP), a specific biomarker of CAFs in cancer progression, its role in the survival of patients with colorectal cancer (CRC) and tumor immune microenvironment (TIME) remains unclear.

We investigated 180 pathological sections obtained from 178 consecutive patients with CRC who underwent surgical resection at Shiga University of Medical Science Hospital between January 2013 and December 2015. FAP expression levels and CD3 and CD8 densities at the invasive margin and center of tumor were assessed using immunohistochemical (IHC) staining. Furthermore, we used single-cell RNA sequencing (scRNA-seq) of CAFs in a separate cohort of 10 untreated patients with CRC derived from the Gene Expression Omnibus database.

According to IHC evaluation, high FAP expression in patients with CRC showed a correlation with reduced tumor-infiltrating lymphocyte (TIL) distribution and poor survival. Based on the FAP transcription levels obtained through scRNA-seq analysis, CAFs were grouped into high and low FAP expression groups. Elevated FAP expression was correlated with decreased expression of T- and B-cell biomarkers, suggesting an association with an immunosuppressive TME promotion. Several genes associated with cancer-related immune-mediated pathways (CXCL12, COL11A1, CCL11, and COL10A1) were significantly upregulated in FAP-positive CAFs.

This study highlights the effects of FAP expression on survival of patients with CRC, its interaction with TILs, and relevant signaling pathways, and underscores potential immunotherapeutic targets for future investigation.

Atrial fibrillation (AF) is a predominant cardiac arrhythmia with unclear etiology. This study used bioinformatics and machine learning to explore the relationship between mitochondrial energy metabolism-related genes (MEMRGs) and immune infiltration in AF. The datasets GSE31821, GSE41177, and GSE79768 were retrieved from the Gene Expression Omnibus (GEO) database, and differential expression analysis identified 59 mitochondrial energy metabolism-related differentially expressed genes (MEMRDEGs) associated with AF. Key MEMRDEGs were selected using the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) methods, and a diagnostic model was developed. Immune infiltration was assessed using single-sample gene set enrichment analysis (ssGSEA) and the microenvironment cell population counter (MCPcounter). The diagnostic model, based on the key genes ACAT1, ALDH1L2, HTT, OGDH, and SLC25A3, achieved an area under the curve (AUC) of 0.903. Significant differences in immune cell composition were observed between the AF and control groups. ALDH1L2 was positively correlated with most immune cells, while SLC25A3 showed a negatively correlated with the monocytic lineage. The findings indicate that MEMRGs interact with immune responses in AF, offering insights into the potential molecular mechanisms and therapeutic targets for AF.

The role of cancer-associated fibroblasts (CAFs) in modulating the tumor microenvironment (TME) is gaining attention, yet their impact on prognosis and therapeutic response in colon cancer remains unclear. Here, we identified genes associated with CAF infiltration via weighted gene co-expression network analysis (WGCNA) utilizing data from The Cancer Genome Atlas (TCGA) and GSE39582 cohorts. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct CAF molecular signatures (CAFscore). Patients were categorized into high and low CAFscore groups to analyze clinicopathological traits, somatic mutations, immune evasion, and treatment responses. In this study, a total of 244 genes were correlated with CAF infiltration, with 11 linked to overall survival. Notably, FSTL3, CRIP2, and SLC2A3 were selected for the CAFscore. A higher CAFscore was associated with poorer prognoses, increased malignancy, and therapeutic resistance, particularly among patients with high tumor mutation burden and microsatellite instability. Furthermore, elevated FSTL3 expression was associated with reduced CD8+ T cell infiltration, indicating a suppressive TME. Mechanistically, CAFs may promote immune evasion via NAMPT ligand-receptor interactions based on single-cell RNA sequencing data. Thus, the CAFscore is crucial for personalizing treatment strategies and identifying patients who require more aggressive management.

In the context of osteoarthritis (OA), a condition marked by joint degeneration, there is a notable absence of efficacious approaches to promote regenerative healing in chondrocytes. Novel therapeutic strategies like nanomicelles-hydrogel microspheres loaded with Astragalus polysaccharide (GelMA@APPA) offer promising avenues for promoting chondrocyte regeneration and mitigating OA progression.

Astragalus polysaccharide (APS) has been shown to induce chondrocyte proliferation and promote cartilage matrix secretion, demonstrating biological activity associated with chondrocyte regeneration. However, the clinical efficacy of APS remains uncertain. Therefore, this investigation validated the beneficial impact of APS on reducing knee joint damage severity induced by destabilization of the medial meniscus (DMM) in mice. The application of bioinformatics analysis and in vitro experimentation revealed that fibroblast growth factor receptor 2 (Fgfr2) in chondrocytes is a key target protein for APS in ameliorating OA-induced cartilage injury, as the deletion of chondrocyte Fgfr2 resulted in the complete loss of the therapeutic effect of APS. To enhance the efficacy of APS, we incorporated APS into nanoparticle-laden hydrogel microspheres to further bolster its potential in chondrocyte regeneration therapy. Subsequently, we developed GelMA@APPA, which exhibited no significant cytotoxic effects on normal chondrocytes in vitro and could be efficiently internalized by chondrocytes. Following subsequent in vitro and in vivo experiments, we affirmed the beneficial effects of GelMA@APPA on OA mice and cartilage cells damaged by OA, as well as its enhancement of the therapeutic effects of APS.

APS significantly improved knee joint injuries in OA mice. Bioinformatics and in vitro analyses identified Fgfr2 as a critical target protein for APS's regenerative effects. Disruption of Fgfr2 negated APS's benefits. GelMA@APPA demonstrated good biocompatibility, effective internalization by chondrocytes, and enhanced the therapeutic efficacy of APS in experiments conducted both in vitro and in vivo, improving chondrocyte proliferation and reducing apoptosis.

This study demonstrates that GelMA@APPA microspheres effectively promote chondrocyte regeneration and OA treatment by activating Fgfr2. These findings suggest a novel therapeutic mechanism for OA and lay the groundwork for future clinical utilization of GelMA@APPA in regenerative medicine.

In this study, we identified cancer-associated fibroblast (CAF) molecular subtypes and developed a CAF-based prognostic model for breast cancer (BRCA). The heterogeneity of cancer-associated fibroblasts (CAFs) and their significant involvement in the advancement of BRCA were discovered employing single-cell RNA sequencing. Notably, we discovered that the RUNX1/SDC1 axis enhances BRCA cell invasion and metastasis. RUNX1 transcriptionally upregulates SDC1, which facilitates extracellular matrix remodeling and promotes tumor cell migration. This finding highlights the vital contribution of CAFs to the tumor microenvironment and provides new potential targets for therapeutic intervention. The predictive model showcased remarkable precision in anticipating patient outcomes and could guide personalized treatment strategies.

This study aims to investigate the role of neuronal SIRPα and microglial synaptic remodeling in sevoflurane-induced cognitive dysfunction in newborn mice. Newborn mice were exposed to sevoflurane, followed by behavioral assessments and single-cell transcriptome sequencing of cortical cells. Lentivirus-mediated overexpression of neuronal SIRPα and assessment of the microglial morphology and synaptic function were conducted. Sevoflurane exposure resulted in social cognitive impairments without affecting motor coordination. Transcriptomic analysis revealed no significant changes in cortical microglial cells or neurons. However, sevoflurane inhibited nonsynaptic synapse modification by microglia. Overexpression of neuronal SIRPα enhanced microglial function, promoted neuron development, and ameliorated cognitive impairments. SCENIC analysis identified a correlation between IRF8 and SIRPα expression. This study sheds light on the involvement of neuronal SIRPα and microglial synaptic remodeling in sevoflurane-induced cognitive dysfunction. Understanding these mechanisms offers new avenues for exploring cognitive impairment pathways and potential therapeutic targets.

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

The abundance and biological contribution of natural killer (NK) cells in cancer are controversial. Here, we aim to uncover clinical relevance and cellular roles of NK cells in colon cancer liver metastasis (CCLM). Here, we integrated single-cell RNA-sequencing, spatial transcriptomics (ST), and bulk RNA-sequencing datasets to investigate NK cells' biological properties and functions in the microenvironment of primary and liver metastatic tumors. Results were validated through an in vitro co-culture experiment based on bioinformatics analysis. Useing single-cell RNA-sequencing and ST, we mapped the immune cellular landscape of colon cancer and well-matched liver metastatic cancer. We discovered that GZMK+ resting NK cells increased significantly in tumor tissues and were enriched in the tumor regions of both diseases. After combining bulk RNA and clinical data, we observed that these NK cell subsets contributed to a worse prognosis. Meanwhile, KIR2DL4+ activated NK cells exhibited the opposite position and relevance. Pseudotime cell trajectory analysis revealed the evolution of activated to resting NK cells. In vitro experiments further confirmed that tumor-cell-co-cultured NK cells exhibited a decidual-like status, as evidenced by remarkable increasing CD9 expression. Functional experiments finally revealed that NK cells exhibited tumor-activating characteristics by promoting the dissociation of SCF (stem cell factor) on the tumor cells membrane depending on cell-to-cell interaction, as the supernatant of the co-culture system enhanced tumor progression. In summary, our findings revealed resting NK cells exhibited a clinical relevance with CCLM, which may be exploited for novel strategies to improve therapeutic outcomes for patients with CCLM.

Colorectal cancer (CRC) presents a growing concern globally, marked by its escalating incidence and mortality rates, thus imposing a substantial health burden. This investigation delves into the role of nuclear receptor subfamily 3 group C member 1 (NR3C1) in CRC metastasis and explores the associated mechanism. Through a comprehensive bioinformatics analysis, NR3C1 emerged as a gene with diminished expression levels in CRC. This finding was corroborated by observations of a low-expression pattern of NR3C1 in both CRC tissues and cells. Furthermore, experiments involving NR3C1 knockdown revealed an exacerbation of proliferation, migration, and invasion of CRC cells in vitro. Subsequent assessments in mouse xenograft tumor models, established by injecting human HCT116 cells either through the tail vein or at the cecum termini, demonstrated a reduction in tumor metastasis to the lung and liver, respectively, upon NR3C1 knockdown. Functionally, NR3C1 (glucocorticoid receptor) suppressed SET binding protein 1 (SETBP1) transcription by binding to its promoter region. Notably, mouse double minute 4 (MDM4) was identified as an upstream regulator of NR3C1, orchestrating its downregulation via ubiquitination-dependent proteasomal degradation. Further investigations unveiled that SETBP1 knockdown suppressed migration and invasion, and epithelial to mesenchymal transition of CRC cells, consequently impeding in vivo metastasis in murine models. Conversely, upregulation of MDM4 exacerbated the metastatic phenotype of CRC cells, a propensity mitigated upon additional upregulation of NR3C1. In summary, this study elucidates a cascade wherein MDM4-mediated ubiquitination of NR3C1 enables the transcriptional activation of SETBP1, thereby propelling the dissemination of CRC cells.

Hepatic ischemia-reperfusion injury (HIRI) may cause severe hepatic impairment, acute hepatic insufficiency, and multiorgan system collapse. Exosomes can alleviate HIRI. Therefore, this study explored the role of exosomal-related genes (ERGs) in HIRI using bioinformatics to determine the underlying molecular mechanisms and novel diagnostic markers for HIRI. We merged the GSE12720, GSE14951, and GSE15480 datasets obtained from the Gene Expression Omnibus (GEO) database into a combined gene dataset (CGD). CGD was used to identify differentially expressed genes (DEGs) based on a comparison of the HIRI and healthy control cohorts. The impact of these DEGs on HIRI was assessed through gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). ERGs were retrieved from the GeneCards database and prior studies, and overlapped with the identified DEGs to yield the set of exosome-related differentially expressed genes (ERDEGs). Functional annotations and enrichment pathways of these genes were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Diagnostic models for HIRI were developed using least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM) algorithms. Key genes with diagnostic value were identified from the overlap, and single-sample gene-set enrichment analysis (ssGSEA) was conducted to evaluate the immune infiltration characteristics. A molecular regulatory interaction network was established using Cytoscape software to elucidate the intricate regulatory mechanisms of key genes in HIRI. Finally, exosome score (Es) was obtained using ssGSEA and the HIRI group was divided into the Es_High and Es_Low groups based on the median Es. Gene expression was analyzed to understand the impact of all genes in the CGD on HIRI. Finally, the relative expression levels of the five key genes in the hypoxia-reoxygenation (H/R) model were determined using quantitative real-time PCR (qRT-PCR). A total of 3810 DEGs were identified through differential expression analysis of the CGD, and 61 of these ERDEGs were screened. Based on GO and KEGG enrichment analyses, the ERDEGs were mainly enriched in wound healing, MAPK, protein kinase B signaling, and other pathways. GSEA and GSVA revealed that these genes were mainly enriched in the TP53, MAPK, TGF[Formula: see text], JAK-STAT, MAPK, and NFKB pathways. Five key genes (ANXA1, HNRNPA2B1, ICAM1, PTEN, and THBS1) with diagnostic value were screened using the LASSO regression and SVM algorithms and their molecular interaction network was established using Cytoscape software. Based on ssGSEA, substantial variations were found in the expression of 18 immune cell types among the groups (p < 0.05). Finally, the Es of each HIRI patient was calculated. ERDEGs in the Es_High and Es_Low groups were enriched in the IL18, TP53, MAPK, TGF[Formula: see text], and JAK-STAT pathways. The differential expression of these five key genes in the H/R model was verified using qRT-PCR. Herein, five key genes were identified as potential diagnostic markers. Moreover, the potential impact of these genes on pathways and the regulatory mechanisms of their interaction network in HIRI were revealed. Altogether, our findings may serve as a theoretical foundation for enhancing clinical diagnosis and elucidating underlying pathogeneses.

Tuberculosis (TB) is a global infectious threat, and the emergence of multidrug-resistant TB has become a major challenge in eradicating the disease that requires the discovery of new treatment strategies. This study aimed to elucidate the immune infiltration and molecular regulatory network of T cell-interacting activating receptors on myeloid cell 1 (TARM1)-related genes based on a bioinformatics analysis. The GSE114911 dataset was obtained from the Gene Expression Omnibus (GEO) and screened to identify 17 TARM1-related differentially expressed genes (TRDEGs). Genes interacting with the TRDEGs were analyzed using a Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A gene set enrichment analysis (GSEA) was used to identify the biological pathways significantly associated with a Mycobacterium tuberculosis (Mtb) infection. The key genes were obtained based on Cytoscape's cytoHubba plug-in. Furthermore, protein-protein interaction (PPI) networks were analyzed through STRING, while mRNA-RNA-binding protein (RBP) and mRNA-transcription factor (TF) interaction networks were developed utilizing the StarBase v3.0 and ChIPBase databases. In addition, the diagnostic significance of key genes was evaluated via receiver operating characteristic (ROC) curves, and the immune infiltration was analyzed using an ssGSEA and MCPCounter. The key genes identified in the GSE114911 dataset were confirmed in an independent GSE139825 dataset. A total of seventeen TRDEGs and eight key genes were obtained in a differential expression analysis using the cytoHubba plug-in. Through the GO and KEGG analysis, it was found that these were involved in the NF-κB, PI3K/Akt, MAPK, and other pathways related to inflammation and energy metabolism. Furthermore, the ssGSEA and MCPCounter analysis revealed a significant rise in activated T cells and T helper cells within the Mtb infection group, which were markedly associated with these key genes. This implies their potential significance in the anti-Mtb response. In summary, our results show that TRDEGs are linked to inflammation, energy metabolism, and immune cells, offering fresh insights into the mechanisms underlying TB pathogenesis and supporting further investigation into the possible molecular roles of TARM1 in TB, as well as assisting in the identification of prospective diagnostic biomarkers.

To assess the efficacy of a novel 3D biomimetic hydrogel scaffold with immunomodulatory properties in promoting fracture healing. Immunomodulatory scaffolds were used in cell experiments, osteotomy mice treatment, and single-cell transcriptomic sequencing. In vitro, fluorescence tracing examined macrophage mitochondrial transfer and osteogenic differentiation of bone marrow-derived mesenchymal stem cells (BMSCs). Scaffold efficacy was assessed through alkaline phosphatase (ALP), Alizarin Red S (ARS) staining, and in vivo experiments. The scaffold demonstrated excellent biocompatibility and antioxidant-immune regulation. Single-cell sequencing revealed a shift in macrophage distribution towards the M2 phenotype. In vitro experiments showed that macrophage mitochondria promoted BMSCs' osteogenic differentiation. In vivo experiments confirmed accelerated fracture healing. The GAD/Ag-pIO scaffold enhances osteogenic differentiation and fracture healing through immunomodulation and promotion of macrophage mitochondrial transfer.

The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited.

We conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared.

The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group.

This study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

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.

Cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, the impact of CAF subpopulation trajectory differentiation on CRC remains unclear.

In this study, we first explored the trajectory differences of CAFs subpopulations using bulk and integrated single-cell sequencing data, and then performed consensus clustering of CRC samples based on the trajectory differential genes of CAFs subpopulations. Subsequently, we analyzed the heterogeneity of CRC subtypes using bioinformatics. Finally, we constructed relevant prognostic signature using machine learning and validated them using spatial transcriptomic data.

Based on the differential genes of CAFs subpopulation trajectory differentiation, we identified two CRC subtypes (C1 and C2) in this study. Compared to C1, C2 exhibited worse prognosis, higher immune evasion microenvironment and high CAF characteristics. C1 was primarily associated with metabolism, while C2 was primarily associated with cell metastasis and immune regulation. By combining 101 combinations of 10 machine learning algorithms, we developed a High-CAF risk signatures (HCAFRS) based on the C2 characteristic gene. HCAFRS was an independent prognostic factor for CRC and, when combined with clinical parameters, significantly predicted the overall survival of CRC patients. HCAFRS was closely associated with epithelial-mesenchymal transition, angiogenesis, and hypoxia. Furthermore, the risk score of HCAFRS was mainly derived from CAFs and was validated in the spatial transcriptomic data.

In conclusion, HCAFRS has the potential to serve as a promising prognostic indicator for CRC, improving the quality of life for CRC patients.

Cancer-associated fibroblasts (CAFs), as significant constituents of the tumor microenvironment (TME), play a pivotal role in the progression of cancers, including colorectal cancer (CRC). In this comprehensive review, we presented the origins and activation mechanisms of CAFs in CRC, elaborating on how CAFs drive tumor progression through their interactions with CRC cells, immune cells, vascular endothelial cells, and the extracellular matrix within the TME. We systematically outline the intricate web of interactions among CAFs, tumor cells, and other TME components, and based on this complex interplay, we summarize various therapeutic strategies designed to target CAFs in CRC. It is also essential to recognize that CAFs represent a highly heterogeneous group, encompassing various subtypes such as myofibroblastic CAF (myCAF), inflammatory CAF (iCAF), antigen-presenting CAF (apCAF), vessel-associated CAF (vCAF). Herein, we provide a summary of studies investigating the heterogeneity of CAFs in CRC and the characteristic expression patterns of each subtype. While the majority of CAFs contribute to the exacerbation of CRC malignancy, recent findings have revealed specific subtypes that exert inhibitory effects on CRC progression. Nevertheless, the comprehensive landscape of CAF heterogeneity still awaits exploration. We also highlight pivotal unanswered questions that need to be addressed before CAFs can be recognized as feasible targets for cancer treatment. In conclusion, the aim of our review is to elucidate the significance and challenges of advancing in-depth research on CAFs, while outlining the pathway to uncover the complex roles of CAFs in CRC and underscore their significant potential as therapeutic targets.

This study developed a prognostic signature for cervical cancer using transcriptome profiling and clinical data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and TISCH database, focusing on cancer-associated fibroblasts (CAFs). Through LASSO Cox regression and integrated bioinformatics analyses, we identified 144 differentially expressed genes (DEGs) related to CAFs, from which an 11-gene CAF-related signature (CAFRSig) was constructed. The CAFRSig effectively stratified patients into high- and low-risk categories, demonstrating significant prognostic capability in predicting overall survival. Gene ontology (GO) and gene set variation analysis (GSVA) linked the DEGs to crucial pathways in tumor malignancy, immune response, and fatty acid metabolism. The immune landscape analysis, utilizing the TIMER platform and CIBERSORT algorithm, revealed a positive correlation between immune cell effector functions and CAFRSig scores, highlighting the model's potential to identify patients likely to respond to immune checkpoint blockade (ICB) therapies. Furthermore, neuropilin 1 (NRP1), a key gene in the CAFRSig, was upregulated in cervical cancer tissues and associated with disease progression and differentiation. The downregulation of NRP1 curbed cell proliferation and influenced the epithelial-mesenchymal transition (EMT), implicating the PI3K/AKT pathway and modulating PD-L1 expression. This comprehensive analysis establishes a robust prognostic signature based on CAF-related genes, offering valuable insights for optimizing therapeutic strategies in cervical cancer management.

This study aimed to investigate the critical role of MDSCs in CRC immune suppression, focusing on the CSF1R and JAK/STAT3 signaling axis. Additionally, it assessed the therapeutic efficacy of LNCs@CSF1R siRNA and anti-PD-1 in combination.

Single-cell transcriptome sequencing data from CRC and adjacent normal tissues identified MDSC-related differentially expressed genes. RNA-seq analysis comprehensively profiled MDSC gene expression in murine CRC tumors. LNCs@CSF1R siRNA nanocarriers effectively targeted and inhibited CSF1R. Flow cytometry quantified changes in MDSC surface markers post-CSF1R inhibition. RNA-seq and pathway enrichment analyses revealed the impact of CSF1R on MDSC metabolism and signaling. The effect of CSF1R inhibition on the JAK/STAT3 signaling axis was validated using Colivelin and metabolic assessments. Glucose and fatty acid uptake were measured via fluorescence-based flow cytometry. The efficacy of LNCs@CSF1R siRNA and anti-PD-1, alone and in combination, was evaluated in a murine CRC model with extensive tumor section analyses.

CSF1R played a significant role in MDSC-mediated immune suppression. LNCs@CSF1R siRNA nanocarriers effectively targeted MDSCs and inhibited CSF1R. CSF1R regulated MDSC fatty acid metabolism and immune suppression through the JAK/STAT3 signaling axis. Inhibition of CSF1R reduced STAT3 activation and target gene expression, which was rescued by Colivelin. Combined treatment with LNCs@CSF1R siRNA and anti-PD-1 significantly slowed tumor growth and reduced MDSC abundance within CRC tumors.

CSF1R via the JAK/STAT3 axis critically regulates MDSCs, particularly in fatty acid metabolism and immune suppression. Combined therapy with LNCs@CSF1R siRNA and anti-PD-1 enhances therapeutic efficacy in a murine CRC model, providing a strong foundation for future clinical applications.

Disruptions in calcium homeostasis are associated with a wide range of diseases, and play a pivotal role in the development of cancer. However, the construction of prognostic models using calcium extrusion-related genes in colon adenocarcinoma (COAD) has not been well studied. We aimed to identify whether calcium extrusion-related genes serve as a potential prognostic biomarker in the COAD progression.

We constructed a prognostic model based on the expression of calcium extrusion-related genes (SLC8A1, SLC8A2, SLC8A3, SLC8B1, SLC24A2, SLC24A3 and SLC24A4) in COAD. Subsequently, we evaluated the associations between the risk score calculated by calcium extrusion-related genes and mutation signature, immune cell infiltration, and immune checkpoint molecules. Then we calculated the immune score, stromal score, tumor purity and estimate score using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm. The response to immunotherapy was assessed using tumor immune dysfunction and exclusion (TIDE). Finally, colorectal cancer cells migration, growth and colony formation assays were performed in RKO cells with the overexpression or knockdown SLC8A3, SLC24A2, SLC24A3, or SLC24A4.

We found that patients with high risk score of calcium extrusion-related genes tend to have a poorer prognosis than those in the low-risk group. Additionally, patients in high-risk group had higher rates of KRAS mutations and lower MUC16 mutations, implying a strong correlation between KRAS and MUC16 mutations and calcium homeostasis in COAD. Moreover, the high-risk group showed a higher infiltration of regulatory T cells (Tregs) in the tumor microenvironment. Finally, our study identified two previously unreported model genes (SLC8A3 and SLC24A4) that contribute to the growth and migration of colorectal cancer RKO cells.

Altogether, we developed a prognostic risk model for predicting the prognosis of COAD patients based on the expression profiles of calcium extrusion-related genes, Furthermore, we validated two previously unreported tumor suppressor genes (SLC8A3 and SLC24A4) involved in colorectal cancer progression.

Various factors, including fatty liver and macrophage alterations, influence colorectal cancer (CRC). This study explores the mechanistic role of fatty liver in CRC progression, focusing on macrophage polarization and lipid metabolism. A murine fatty liver model was created with a high-fat diet (HFD), and CRC was induced using AOM and DSS. Single-cell transcriptome sequencing (scRNA-seq) identified MAPKAP1 as a critical gene promoting CRC via M2 macrophage polarization and lipid metabolism reprogramming. Prognosis analysis on the TCGA-CRC dataset confirmed MAPKAP1's significance. In vitro and in vivo experiments demonstrated that EVs from fatty liver cells enhanced MAPKAP1 expression, accelerating CRC development and metastasis. HFD exacerbated CRC, but fatty acid inhibitors delayed progression. Fatty liver upregulates MAPKAP1, driving M2 macrophage polarization and lipid metabolism changes, worsening CRC. These findings suggest potential therapeutic strategies for CRC, particularly targeting lipid metabolism and macrophage-mediated tumor promotion.

Predicting the response to neoadjuvant chemoradiotherapy (nCRT) before initiating treatment is essential for tailoring therapeutic strategies and monitoring prognosis in locally advanced rectal cancer (LARC). In this study, we aimed to develop and validate radiomic-based models to predict clinical and pathological complete responses (cCR and pCR, respectively) by incorporating the Shapley Additive exPlanations (SHAP) method for model interpretation.

A total of 285 patients with complete pretreatment clinical characteristics and T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) at 3 centers were retrospectively recruited. The features of tumor lesions were extracted by PyRadiomics and selected using least absolute shrinkage and selection operator (LASSO) algorithm. The selected features were used to build multilayer perceptron (MLP) models alone or combined with clinical features. Area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were applied to evaluate performance of models. The SHAP method was adopted to explain the prediction models.

The radiomic-based models all showed better performances than clinical models. The clinical-radiomic models showed the best differentiation on cCR and pCR with mean AUCs of 0.718 and 0.810 in the validation set, respectively. The decision curves of the clinical-radiomic models showed its values in clinical application. The SHAP method powerfully interpreted the prediction models both at a holistic and individual levels.

Our study highlights that the radiomic-based prediction models have more excellent abilities than clinical models and can effectively predict treatment response and optimize therapeutic strategies for patients with LARCs.

Pancreatic ductal adenocarcinoma (PDAC) is renowned for its formidable and lethal nature, earning it a notorious reputation among malignant tumors. Due to its challenging early diagnosis, high malignancy, and resistance to chemotherapy drugs, the treatment of pancreatic cancer has long been exceedingly difficult in the realm of oncology. γ-Glutamyl cyclotransferase (GGCT), a vital enzyme in glutathione metabolism, has been implicated in the proliferation and progression of several tumor types, while the biological function of GGCT in pancreatic ductal adenocarcinoma remains unknown.

The expression profile of GGCT was validated through western blotting, immunohistochemistry, and RT-qPCR in both pancreatic cancer tissue samples and cell lines. Functional enrichment analyses including GSVA, ssGSEA, GO, and KEGG were conducted to explore the biological role of GGCT. Additionally, CCK8, Edu, colony formation, migration, and invasion assays were employed to evaluate the impact of GGCT on the proliferation and migration abilities of pancreatic cancer cells. Furthermore, the LASSO machine learning algorithm was utilized to develop a prognostic model associated with GGCT.

Our study revealed heightened expression of GGCT in pancreatic cancer tissues and cells, suggesting an association with poorer patient prognosis. Additionally, we explored the immunomodulatory effects of GGCT in both pan-cancer and pancreatic cancer contexts, found that GGCT may be associated with immunosuppressive regulation in various types of tumors. Specifically, in patients with high expression of GGCT in pancreatic cancer, there is a reduction in the infiltration of various immune cells, leading to poorer responsiveness to immunotherapy and worse survival rates. In vivo and in vitro assays indicate that downregulation of GGCT markedly suppresses the proliferation and metastasis of pancreatic cancer cells. Moreover, this inhibitory effect appears to be linked to the regulation of GGCT on c-Myc. A prognostic model was constructed based on genes derived from GGCT, demonstrating robust predictive ability for favorable survival prognosis and response to immunotherapy.

The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer (CRC), one of the most prevalent malignancies worldwide. In this study, multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC. By revealing the heterogeneity and functional differences of B cells in cancer immunity, we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies.

To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC, explore the potential driving mechanism of B cell development, analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules, and search for possible regulatory pathways to promote the anti-tumor effects of B cells.

A total of 69 paracancer (normal), tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/). After the immune cells were sorted by multicolor flow cytometry, the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform, and the data were analyzed using bioinformatics tools such as Seurat. The differences in the number and function of B cell infiltration between tumor and normal tissue, the interaction between B cell subsets and T cells and myeloid cell subsets, and the transcription factor regulatory network of B cell subsets were explored and analyzed.

Compared with normal tissue, the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly. Among them, germinal center B cells (GCB) played the most prominent role, with positive clone expansion and heavy chain mutation level increasing, and the trend of differentiation into memory B cells increased. However, the number of plasma cells in the tumor microenvironment decreased significantly, and the plasma cells secreting IgA antibodies decreased most obviously. In addition, compared with the immune microenvironment of normal tissues, GCB cells in tumor tissues became more closely connected with other immune cells such as T cells, and communication molecules that positively regulate immune function were significantly enriched.

The role of GCB in CRC tumor microenvironment is greatly enhanced, and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level. Meanwhile, GCB has enhanced its association with immune cells in the microenvironment, which plays a positive anti-tumor effect.

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.

Tumor invasion and metastasis are the processes that primarily cause adverse outcomes in patients with cervical cancer. Cancer-associated fibroblasts (CAFs), which participate in cancer progression and metastasis, are novel targets for the treatment of tumors. The present study aimed to assess the heterogeneity of CAFs in the cervical cancer microenvironment through single-cell RNA sequencing. After collecting five cervical cancer samples and obtaining the CAF-associated gene sets, the CAFs in the cervical cancer microenvironment were divided into myofibroblastic CAFs and extracellular (ec)CAFs. The ecCAFs appeared with more robust pro-tumorigenic effects than myCAFs according to enrichment analysis. Subsequently, through combining the ecCAF hub genes and bulk gene expression data for cervical cancer obtained from The Cancer Genome Atlas and Gene Ontology databases, univariate Cox regression and least absolute shrinkage and selection operator analyses were performed to establish a CAF-associated risk signature for patients with cancer. The established risk signature demonstrated a stable and strong prognostic capability in both the training and validation cohorts. Subsequently, the association between the risk signature and clinical data was evaluated, and a nomogram to facilitate clinical application was established. The risk score was demonstrated to be associated with both the tumor immune microenvironment and the therapeutic responses. Moreover, the signature also has predictive value for the prognosis of head and neck squamous cell carcinoma, and bladder urothelial carcinoma, which were also associated with human papillomavirus infection. In conclusion, the present study assessed the heterogeneity of CAFs in the cervical cancer microenvironment, and a subgroup of CAFs that may be closely associated with tumor progression was defined. Moreover, a signature based on the hub genes of ecCAFs was shown to have biomarker functionality in terms of predicting survival rates, and therefore this CAF subgroup may become a therapeutic target for cervical cancer in the future.