Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation

Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis

Background

Atherosclerosis is a significant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the timely and accurate detection of early-stage atherosclerosis. Abnormal lipid metabolism plays a critical role in the development of atherosclerosis. Consequently, the identification of new diagnostic markers is essential for the precise diagnosis of this condition.

Method

The datasets related to atherosclerosis utilized in this research were obtained from the GEO database (GSE2470, GSE24495, GSE100927 and GSE43292). The ssGSEA technique was first utilized to assess lipid metabolism scores in samples affected by atherosclerosis, thereby aiding in the discovery of important regulatory genes linked to lipid metabolism via WGCNA. Following this, differential expression analysis and functional evaluations were carried out, after which various machine learning approaches were employed to determine significant diagnostic genes for atherosclerosis. A diagnostic model was then developed and validated through several machine learning algorithms. Furthermore, molecular docking studies were conducted to analyze the binding affinity of these key markers with therapeutic agents for atherosclerosis. The ssGSEA technique was also used to measure immune cell scores in atherosclerotic samples, aiding the exploration of the connection between key diagnostic markers and immune cells. Finally, the expression variations of the identified pivotal genes were confirmed through experimental validation.

Result

WGCNA identified 302 lipid metabolism-related genes in atherosclerotic samples, and functional analysis revealed that these genes are associated with multiple immune pathways. Through further differential analysis and screening using machine learning algorithms, APLNR, PCDH12, PODXL, SLC40A1, TM4SF18, and TNFRSF25 were identified as key diagnostic genes for atherosclerosis. The diagnostic model we constructed was confirmed to predict the occurrence of atherosclerosis with high accuracy, and molecular docking studies indicated that these six key diagnostic genes have potential as drug targets. Additionally, the ssGSEA algorithm further validated the association of these diagnostic genes with various immune cells. Finally, the expression levels of these six genes were experimentally confirmed.

Conclusion

Our study introduces novel lipid metabolism-related diagnostic markers for atherosclerosis and emphasizes their potential as immune-related drug targets. This research provides a valuable approach for the predictive diagnosis and targeted therapy of atherosclerosis.

Heterogeneity of cancer-associated fibroblast subpopulations in prostate cancer: Implications for prognosis and immunotherapy

BACKGROUND

Prostate cancer stands as the second most common malignancy among men, notorious for its intricate heterogeneity, especially evident in metastatic disease. This complexity presents substantial challenges in treatment efficacy and patient prognosis.

OBJECTIVE

This study endeavors to elucidate the multifaceted roles of cancer-associated fibroblasts within the tumor microenvironment of prostate cancer, with a focus on their implications for disease prognosis and the potential for novel immunotherapeutic strategies.

METHODS

Leveraging advanced single-cell RNA sequencing technology, we meticulously characterized the diverse CAF subpopulations within prostate cancer samples. Our analysis identified four predominant subsets: C0 IER2+, C1 ABCA8+, C2 ABI3BP+, and C3 MEOX2+. We conducted comprehensive gene expression profiling to construct a robust prognostic model reflecting the clinical relevance of these subpopulations.

RESULTS

C1 ABCA8+ fibroblasts demonstrated heightened proliferative activity, underscoring their pivotal role in fostering tumor growth and metastasis via intricate signaling pathways. In vitro experiments verified that the T transcription factor NFAT5 of C1 ABCA8+ fibroblasts subpopulation was knocked down in LNCaP clone FGC and 22Rv1 cell lines, which was closely related to the proliferation of PC. Moreover, we identified key genes linked to patient outcomes and immune landscape alterations, reinforcing the prognostic significance of CAF characteristics in this context.

CONCLUSION

This investigation illuminates the critical potential of targeting CAFs to augment immunotherapeutic approaches in prostate cancer. Our findings contribute to a deeper understanding of the TME's complexity, advocating for further exploration into CAF-targeted therapies aimed at enhancing treatment responses and ultimately improving patient outcomes.

Disulfidptosis as a key regulator of glioblastoma progression and immune cell impairment

Background

Glioblastoma, associated with poor prognosis and impaired immune function, shows potential interactions between newly identified disulfidptosis mechanisms and T cell exhaustion, yet these remain understudied.

Methods

Key genes were identified using Lasso regression, followed by multivariate analysis to develop a prognostic model. Single-cell pseudotemporal analysis explored disulfidptosis T-cell exhaustion (Tex) signaling in cell differentiation. Immune infiltration was assessed via ssGSEA, while transwell assays and immunofluorescence examined the effects of disulfidptosis-Tex genes on glioma cell behavior and immune response.

Results

Eleven disulfidptosis-Tex genes were found critical for glioblastoma survival outcomes. This gene set underpinned a model predicting patient prognosis. Single-cell analysis showed high disulfidptosis-Tex activity in endothelial cells. Memory T cell populations were linked to these genes. SMC4 inhibition reduced LN299 cell migration and increased chemotherapy sensitivity, decreasing CD4 and CD8 T cell activation.

Conclusions

Disulfidptosis-Tex genes are pivotal in glioblastoma progression and immune interactions, offering new avenues for improving anti-glioblastoma therapies through modulation of T cell exhaustion.

Single-cell RNA sequencing revealed PPARG promoted osteosarcoma progression: based on osteoclast proliferation

Background

Osteosarcoma (OS) is one of the most common primary malignant bone tumors, primarily originating from mesenchymal tissue. It is notorious for its high invasiveness, high disability rate, high mortality rate, and poor prognosis. In most primary and metastatic malignant tumors, bone destruction can promote cancer progression, which is closely related to osteoclast activation and the imbalance between osteoblasts and osteoclasts. A large number of studies confirmed that osteoclasts are an important part of OS, which play an active role in destroying bone homeostasis and promoting the progress of OS. Therefore, we conducted a detailed study of osteoclasts at the single cell level, aiming to find new OS therapeutic targets to prevent tumor progression and local spread.

Methods

We analyzed the single-cell sequencing data of OS patients and usedMonocle2, Cytotrace, and Slingshot software to analyze the pseudo-sequential trajectory during OS progression. CellChat was used to reveal the communication between cells. PySCENIC was used to identify active transcription factors in osteoclasts. Finally, we further demonstrated the results by RT-qPCR analysis, CCK-8 assay, wound healing assay, Transwell assay, etc.

Results

Through the analysis of single-cell sequencing data in OS, we identified a highly specific subgroup, C2MKI67+ Osteoclast. The key signaling pathway APP and the top 1 transcription factor PPARG in this subgroup played essential roles in osteoclast proliferation and differentiation. Given the pivotal role of osteoclasts in OS progression, we speculated that these signaling pathways and transcription factors could emerge as novel therapeutic targets, offering innovative strategies for OS treatment.

Conclusion

This study enhanced our understanding of OS and osteoclasts through scRNA-seq. Furthermore, we discovered that PPARG amplifies osteoclast activation and proliferation, resulting in excessive bone resorption and degradation of the bone matrix, thereby creating a favorable environment for tumor cell proliferation and growth. By innovatively targeting PPARG, it affected osteoclast proliferation and thus affected tumor progression; this work offered new insights and directions for the clinical treatment of OS patients.

Characterizing tumor biology and immune microenvironment in high-grade serous ovarian cancer via single-cell RNA sequencing: insights for targeted and personalized immunotherapy strategies

Background

High-grade serous ovarian cancer (HGSOC), the predominant subtype of epithelial ovarian cancer, is frequently diagnosed at an advanced stage due to its nonspecific early symptoms. Despite standard treatments, including cytoreductive surgery and platinum-based chemotherapy, significant improvements in survival have been limited. Understanding the molecular mechanisms, immune landscape, and drug sensitivity of HGSOC is crucial for developing more effective and personalized therapies. This study integrates insights from cancer immunology, molecular profiling, and drug sensitivity analysis to identify novel therapeutic targets and improve treatment outcomes. Utilizing single-cell RNA sequencing (scRNA-seq), the study systematically examines tumor heterogeneity and immune microenvironment, focusing on biomarkers influencing drug response and immune activity, aiming to enhance patient outcomes and quality of life.

Methods

scRNA-seq data was obtained from the GEO database in this study. Differential gene expression was analyzed using gene ontology and gene set enrichment methods. InferCNV identified malignant epithelial cells, while Monocle, Cytotrace, and Slingshot software inferred subtype differentiation trajectories. The CellChat software package predicted cellular communication between malignant cell subtypes and other cells, while pySCENIC analysis was utilized to identify transcription factor regulatory networks within malignant cell subtypes. Finally, the analysis results were validated through functional experiments, and a prognostic model was developed to assess prognosis, immune infiltration, and drug sensitivity across various risk groups.

Results

This study investigated the cellular heterogeneity of HGSOC using scRNA-seq, focusing on tumor cell subtypes and their interactions within the tumor microenvironment. We confirmed the key role of the C2 IGF2+ tumor cell subtype in HGSOC, which was significantly associated with poor prognosis and high levels of chromosomal copy number variations. This subtype was located at the terminal differentiation of the tumor, displaying a higher degree of malignancy and close association with stage IIIC tissue types. The C2 subtype was also associated with various metabolic pathways, such as glycolysis and riboflavin metabolism, as well as programmed cell death processes. The study highlighted the complex interactions between the C2 subtype and fibroblasts through the MK signaling pathway, which may be closely related to tumor-associated fibroblasts and tumor progression. Elevated expression of PRRX1 was significantly connected to the C2 subtype and may impact disease progression by modulating gene transcription. A prognostic model based on the C2 subtype demonstrated its association with adverse prognosis outcomes, emphasizing the importance of immune infiltration and drug sensitivity analysis in clinical intervention strategies.

Conclusion

This study integrates molecular oncology, immunotherapy, and drug sensitivity analysis to reveal the mechanisms driving HGSOC progression and treatment resistance. The C2 IGF2+ tumor subtype, linked to poor prognosis, offers a promising target for future therapies. Emphasizing immune infiltration and drug sensitivity, the research highlights personalized strategies to improve survival and quality of life for HGSOC patients.

Intratumor microbiome-derived butyrate promotes chemo-resistance in colorectal cancer

Introduction

Colorectal cancer (CRC) is a leading cause of cancer-related mortality globally. Although tumor immunotherapy is widely recognized for treating unresectable CRC, challenges such as ineffective immunotherapy and drug resistance remain prevalent. While intratumor microbiome-derived butyrate has been implicated in promoting lung cancer metastasis, its role in CRC chemoresistance is not well understood. This study aimed to explore the relationship between intratumor butyrate and chemoresistance in CRC.

Methods

We performed a comprehensive analysis of the microbiome composition in CRC patients with varying resistance-free survival (RFS) durations, utilizing 16S rRNA sequencing. Furthermore, we assessed the prognostic significance of circulating microbiome DNA (cmDNA) and examined the effects of exogenous butyrate supplementation on the chemosensitivity of CRC cell lines.

Results

Our 16S sequencing analysis revealed a reduction in microbial diversity within tumor samples of patients with resistance, as indicated by metrics such as observed taxonomic units, Shannon, and Simpson indices. Notably, Roseburia and Fusobacteria emerged as prominent biomarkers for the resistance group, whereas Bifidobacterium, Helicobacter, and Akkermansia were identified as biomarkers for the non-resistant group. Utilizing a Lasso regression model, we identified six genera-Roseburia, Helicobacter, Gardnerella, Flavonifractor, Coprococcus, and Anaerostipes-that significantly correlated with recurrence-free survival. Furthermore, both the intratumor microbiome signature and circulating microbiome DNA were effective in accurately predicting CRC resistance. Experimental assays, including CCK8 and wound-healing, demonstrated that intratumor microbiome-derived butyrate enhances the proliferation and migration of HCT15 cells in a time- and concentration-dependent manner. Cell survival analysis further indicated that butyrate treatment significantly increased the IC50 value, suggesting heightened drug resistance in HCT15 cells. Mechanistically, this resistance was attributed to butyrate's activation of the PI3K-AKT signaling pathway.

Conclusion

Our results suggest that intratumor microbiome-derived butyrate contributes to chemoresistance in colorectal cancer, highlighting the potential prognostic and therapeutic significance of the intratumor microbiome.

Prognostic value of combined systemic inflammation response index and prognostic nutritional index in colorectal cancer patients

BACKGROUND

The prognosis of colorectal cancer (CRC) patients is notably influenced by both inflammation and nutritional status. The prognostic nutritional index (PNI) and systemic inflammatory response index (SIRI) have been reported in prognostic studies of various tumors. However, the efficacy of the combination of the two in predicting the prognosis of CRC patients has not been studied.

AIM

To evaluate the effectiveness of PNI and SIRI in predicting the prognosis of patients with CRC.

METHODS

We retrospectively gathered data from 470 CRC patients who underwent feasible radical surgery at Xinjiang Cancer Hospital. The optimal cut-off values for SIRI and PNI, along with their predictive power for survival, were determined through area under the receiver operating characteristic curve using time-dependent receiver operating characteristic analysis. The Kaplan-Meier method and log-rank test were applied to assess prognostic impact, and a multifactorial Cox proportional hazards model was employed for analysis. Additionally, a new model, PSIRI, was developed and assessed for its survival prediction capability.

RESULTS

The optimal cutoff values for PNI and SIRI were determined to be 47.80 and 1.38, respectively. Based on these values, patients were categorized into high PNI and low PNI groups, as well as high SIRI and low SIRI groups. Significant differences in age, T stage, neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) subgroups were observed between the PNI groups in the baseline profile. In the SIRI group, notable differences were found in gender, T stage, nerve invasion, intravascular tumor emboli, NLR, MLR, and PLR subgroups. Both low PNI and high SIRI were identified as independent risk factors for poor prognosis in CRC patients. When combined into the PSIRI model, it was shown that patients with a PSIRI ≤ 1 had a higher risk of death compared to those with a PSIRI of 2.

CONCLUSION

We assessed the impact of PNI and SIRI on the prognostic survival of CRC patients and developed a new model, PSIRI. This model demonstrated superior predictive accuracy, with a concordance index of 0.767.

Unveiling the NEFH+ malignant cell subtype: Insights from single-cell RNA sequencing in prostate cancer progression and tumor microenvironment interactions

Background

Prostate cancer (PCa) is a multifactorial and heterogeneous disease, ranking among the most prevalent malignancies in men. In 2020, there were 1,414,259 new cases of PCa worldwide, accounting for 7.3% of all malignant tumors. The incidence rate of PCa ranks third, following breast cancer and lung cancer. Patients diagnosed with high-grade PCa frequently present with existing or developing metastases, complicating their treatment and resulting in poorer prognoses, particularly for those with bone metastases. Utilizing single-cell RNA sequencing (scRNA-seq), we identified specific malignant cell subtypes that are closely linked to high-grade PCa. By investigating the mechanisms that govern interactions within the tumor microenvironment (TME), we aim to offer new theoretical insights that can enhance the prevention, diagnosis, and treatment of PCa, ultimately striving to improve patient outcomes and quality of life.

Methods

Data on scRNA-seq was obtained from the GEO database. The gene ontology and gene set enrichment analysis were employed to analyze differential expression genes. Using inferCNV analysis to identify malignant epithelial cells. We subsequently employed Monocle, Cytotrace, and Slingshot packages to infer subtype differentiation trajectories. The cellular communication between malignant cell subtypes and other cells was predicted using the CellChat package. Furthermore, we employed pySCENIC to analyze and identify the regulatory networks of transcription factors (TFs) in malignant cell subtypes. The MDA PCa 2b and VCap cell lines were employed to validate the analysis results through cellular functional experiments. In addition, a risk scoring model was developed to assess the variation in clinical characteristics, prognosis, immune infiltration, immune checkpoint, and drug sensitivity.

Results

A malignant cell subtype in PCa with high expression of NEFH was identified through scRNA-seq analysis. This subtype was situated at the differentiation terminal, exhibited a higher level of malignancy, and exhibited characteristics that were more prone to advanced tumor lesions. In addition, our research underscored the intricate interactions that exist within the TME, particularly the interaction between PTN secreted by this subtype and fibroblasts via the NCL receptor. This interaction may be closely associated with cancer-associated fibroblasts and tumor progression. Subsequently, we determined that the NEFH+ malignant cell subtype was significantly correlated with the TF IRX4. This TF is linked to a worse prognosis in PCa and may affect disease progression by regulating gene transcription. Our conclusions were additionally verified through cellular experiments. Furthermore, the prognostic model we developed demonstrated satisfactory predictive performance, with gene sets from the high NmRS group facilitating tumor progression and deterioration. The analysis of immune infiltration was instrumental in the development of clinical intervention strategies and patient prognosis.

Conclusion

By examining the cellular heterogeneity of a unique NEFH+ malignant cell subtype within the PCa microenvironment, we were able to disclose their reciprocal interaction with disease progression. This offers a novel viewpoint on the diagnosis and treatment of PCa.

Fibroblast growth factor receptor risk signature predicts patient prognosis and immunotherapy resistance in colorectal cancer

Background

Fibroblast Growth Factor Receptor (FGFR) signaling is linked with tumor progression and tumor immunoevasion, yet the potential effect of FGFR signature on the prognosis of patient with colorectal cancer (CRC) and response to immune therapy remains elusive.

Methods

The fibroblast growth factor receptor risk signature (FRS) was identified through single-cell RNA sequencing, bulk RNA sequencing, and machine learning techniques. Signaling enrichment analyses were conducted using Gene Set Enrichment Analysis (GSEA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Drugs targeting the FRS were predicted using the Cancer Therapeutics Response Portal (CTRP) and PRISM databases. The analysis of T cell function and the tumor microenvironment (TME) was performed using flow cytometry.

Results

In this study, we characterized the FRS in cancer patients with CRC. By integrating advanced techniques, we identified the FRS and revealed the intricate molecular landscape and diversity of the FRS within the TME. Notably, the FRS effectively predicted unfavorable prognosis and resistance to immunotherapy in CRC patients. Furthermore, PHA-793887, identified as a potential FRS inhibitor by the CTRP and PRISM databases, significantly restructured the immunosuppressive TME and enhanced the antitumor immune response, resulting in a reduced tumor burden in the MC38 murine tumor model.

Conclusion

Together, these data support FRS positively correlates with poor prognosis and therapy resistance. The PHA-793887 could be a potential FRS inhibitor to improving the effectiveness of CRC management via bolstering antitumor immunity.

CXCR6 expression predicts prognosis and immunotherapeutic benefit in muscle-invasive bladder cancer

Background

Increasing evidence suggests that the CXC chemokine receptor 6 (CXCR6) is involved in tumor progression and the regulation of tumor immunity. However, its role in muscle-invasive bladder cancer (MIBC) remains largely unexplored.

Methods

Data from 391 MIBC patients in the TCGA, 212 patients from GEO, 131 patients from our center, 195 patients in the IMvigor210 cohort, and single-cell RNA sequencing (scRNA-seq) data from 9 bladder cancer patients (GSE222315) were analyzed. Additionally, data from the GEPIA 2, TISCH2, TIMER2.0, and UALCAN platforms were utilized to investigate the prognostic and immunotherapeutic significance of CXCR6 in MIBC.

Results

We observed that CXCR6 expression was significantly reduced in bladder cancer tumors and correlated with tumor stage and grade. Low CXCR6 expression was associated with poor recurrence-free survival (RFS) and overall survival (OS) in the TCGA cohort, a finding validated in both the meta-GEO dataset and our center's cohort. Multivariate analysis confirmed that low CXCR6 expression was an independent predictor of poor OS and RFS. A nomogram incorporating CXCR6 expression and other independent prognostic factors was developed to accurately predict 3- and 5-year OS. Gene set enrichment analysis indicated that immune activation-related pathways were significantly enriched in tumors with high CXCR6 expression. CIBERSORT analysis revealed that CXCR6 expression was positively correlated with CD8+ T cells, CD4+ T cells, activated NK cells, M1 macrophages, and activated dendritic cells in TCGA, findings further validated by TIMER2.0. scRNA-seq data showed that CXCR6 was predominantly expressed in T and NK cells and facilitated T/NK-myeloid interaction via the CXCR6-CXCL16 axis. Importantly, CXCL16+ macrophages and dendritic cells recruited CXCR6+ T and NK cells, which exhibited enhanced cytotoxicity, thereby amplifying anti-tumor immunity. Clinically, in the IMvigor210 immunotherapy cohort, higher CXCR6 expression was associated with improved anti-PD-L1 therapeutic outcomes.

Conclusion

Our findings highlight CXCR6 as a critical biomarker for predicting prognosis and immunotherapeutic response in MIBC.

Serum nutritional predictive biomarkers and risk assessment for anastomotic leakage after laparoscopic surgery in rectal cancer patients

BACKGROUND

Anastomotic leakage (AL) is one of the severest complications after laparoscopic surgery for middle/low rectal cancer, significantly impacting patient outcomes. Identifying reliable predictive factors for AL remains a clinical challenge. Serum nutritional biomarkers have been implicated in surgical outcomes but are underexplored as predictive tools for AL in this setting. Our study hypothesizes that preoperative serum levels of prealbumin (PA), albumin (ALB), and transferrin (TRF), along with surgical factors, can accurately predict AL risk.

AIM

To determine the predictive value of preoperative serum nutritional biomarkers for rectal cancer AL following laparoscopic surgery.

METHODS

In the retrospective cohort study carried out at a tertiary cancer center, we examined 560 individuals who underwent laparoscopic procedures for rectal cancer from 2018 to 2022. Preoperative serum levels of PA, ALB, and TRF were measured. We employed multivariate logistic regression to determine the independent risk factors for AL, and a predictive model was constructed and evaluated using receiver operating characteristic curve analysis.

RESULTS

AL occurred in 11.96% of cases, affecting 67 out of 560 patients. Multivariate analysis identified PA, ALB, and TRF as the independent risk factor, each with an odds ratio of 2.621 [95% confidence interval (CI): 1.582-3.812, P = 0.012], 3.982 (95%CI: 1.927-4.887, P = 0.024), and 2.109 (95%CI: 1.162-2.981, P = 0.031), respectively. Tumor location (< 7 cm from anal verge) and intraoperative bleeding ≥ 300 mL also increased AL risk. The predictive model demonstrated an excellent accuracy, achieving an area under the receiver operating characteristic curve of 0.942, a sensitivity of 0.844, and a specificity of 0.922, demonstrating an excellent ability to discriminate.

CONCLUSION

Preoperative serum nutritional biomarkers, combined with surgical factors, reliably predict anastomotic leakage risk after rectal cancer surgery, highlighting their importance in preoperative assessment.

FAS gene expression, prognostic significance and molecular interactions in lung cancer

Introduction

FAS has been implicated in the development of various cancers, but its involvement in lung cancer has not been systematically characterized. In this study, we performed data mining in online tumor databases to investigate the expression, methylation, alterations, protein interactions, co-expression and prognostic significance of FAS in lung cancer.

Method

The expression, prognostic significance and molecular interactions of FAS in lung cancer was mined and analyzed using GENT2, GEPIA2, UALCAN, cBioPortal, STRING, GeneMANIA, UCSC Xena, Enrichr, and OSluca databases. FAS expression was subsequently investigated at the protein level in samples from 578 lung cancer patients to understand its protein-level expression. In vitro validation of FAS gene expression was performed on H1299, H1993, A549 and HBE cell lines.

Result

We found that the expression of FAS was significantly downregulated in both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) compared to normal lung tissue. In addition, we observed a higher level of FAS promoter methylation in LUSC tissue than in normal tissue. FAS alterations were rare (1.9%) in lung cancer samples, with deep deletions being more common than missense mutations, which occurred mainly in the TNFR-like cysteine-rich domain and the death domain. We also identified a list of proteins interacting with FAS and genes co-expressed with FAS, with LUAD having 11 co-expressed genes and LUSC having 90 co-expressed genes. Our results also showed that FAS expression has limited prognostic significance (HR=1.302, 95% CI=0.935-1.139, P=0.530). Protein level investigation revealed that FAS expression varied among individuals, with nTPM values ranging from 5.2 to 67.2.

Conclusion

This study provides valuable insights into the involvements and characteristics of FAS in lung cancer. Further studies are needed to investigate the clinical significance of FAS alterations in lung cancer and to explore the potential of targeting FAS for therapeutic intervention.

Prognostic value and molecular mechanisms of OAS1 in lung adenocarcinoma

BACKGROUND

The expression of 2'-5'-oligoadenylate synthetase 1 (OAS1) in lung cancer has been validated in numerous studies. However, the prognostic value of OAS1 expression in lung adenocarcinoma (LUAD) still remains unclear. This study aimed to reveal the prognostic value and associated molecular mechanisms of OAS1 expression in LUAD.

METHODS

Gene expression data of LUAD were extracted from online databases. Gene and protein expression levels of OAS1 in LUAD and normal samples were revealed, followed by prognostic analysis of OAS1. Next, we conducted a thorough bioinformatics analysis to examine the enrichment of key functional and biological signaling pathways and their correlation with the abundance of immune cells. The independent prognoses, drug responses, and PPI networks associated with OAS1 were analyzed. OAS1 expression was evaluated in LUAD tissues and cell lines. OAS1 was knocked down by siRNA transfection, followed by CCK8, colony formation, and wound-healing assays.

RESULTS

Gene and protein expression levels of OAS1 in LUAD samples were significantly higher than those in normal samples (all P < 0.05). OAS1 stimulation were correlated with poor prognosis, lymph node metastasis, advanced tumor stage, immune cells, and immunomodulators. The prognostic value of OAS1 in LUAD was determined via univariate regression analysis. In total, 10 OAS1-associated genes were revealed via PPI analysis of OAS1, which were primarily enriched in functions, such as the negative regulation of viral genome replication. Transcriptional analysis revealed several OAS1-related interactions, including STAT3-miR-21-OAS1. STAT3 was overexpressed and miR-21 was expressed in LUAD cells. Upregulation of OAS1 protein was determined in LUAD tissues and cell lines. OAS1 knockdown significantly reduced proliferation and migration of LUAD cells.

CONCLUSIONS

OAS1 overexpression influenced survival and immune cell infiltration in patients with LUAD, which might be a potential prognostic gene for LUAD. Moreover, OAS1 contributed to LUAD progression by participating in STAT3-miR-21-OAS1 axis.

Novel tumor gene expression signatures improve the overall survival prediction efficiency over tumor mutation burden and PD-L1 expression in bladder carcinoma with checkpoint blockade immunotherapy

Although immune checkpoint blockade therapy (ICBT) has revolutionized cancer treatment with good therapeutic response in a number of human cancers, including bladder cancer, many cancers still do not respond to ICBT. Analyzing genetic signatures helps the understanding of underlying biological mechanisms. Here, based on two cohorts of bladder cancer patients receiving ICBT, we identified three novel ICBT-associated signatures in the bladder cancer microenvironment, involving genomic stability, angiogenesis and RNA regulatory, which affect PD-L1 expression and patient response to ICBT. The combinations of these signatures with TMB or PD-L1 expression improved the overall survival prediction efficiency over TMB and PD-L1 expression alone for patients receiving ICBT. Moreover, we utilized two methods to search potential drugs or small-molecules that have an impact on ICBT-associated signatures. This study provides new molecular insight into ICBT response of bladder cancer and has the potential to improve the prediction accuracy for patients to benefit from ICBT.

Elevated BEAN1 expression correlates with poor prognosis, immune evasion, and chemotherapy resistance in rectal adenocarcinoma

BACKGROUND

The BEAN1 gene, primarily studied in neurodegenerative diseases, has been scarcely studied in the context of cancers. Our research examines BEAN1 expression specifically in rectal adenocarcinoma (READ) and its association with prognosis, immune evasion, and chemotherapy resistance.

METHODS

Data from TCGA and GEO were analyzed to assess BEAN1 levels across various cancer types, with particular emphasis on READ. Functional enrichment, immune infiltration, and treatment response analyses were conducted, followed by validation using patient tissue samples.

RESULTS

READ tissues exhibited a marked increase in BEAN1 expression compared to normal tissues. Elevated BEAN1 levels were associated with reduced overall survival and increased immune suppression, characterized by elevated M2 macrophage infiltration and reduced CD8+ T cell presence. BEAN1 expression was also linked to higher immune checkpoint genes expression and resistance to immune checkpoint inhibitors and 5-fluorouracil.

CONCLUSION

This research offers initial evidence that BEAN1 is linked to unfavorable prognosis, immune escape, and resistance to chemotherapy in READ. BEAN1 appears to be a promising new biomarker and potential therapeutic target, warranting further investigation into its potential clinical applications in improving treatment outcomes for READ patients.

Unveiling the cellular landscape: insights from single-cell RNA sequencing in multiple myeloma

Objective

The aim of this research was to gain a thorough understanding of the processes involved in cell communication and discover potential indicators for treating multiple myeloma (MM) through the use of single-cell RNA sequencing (scRNA-seq). And explored the expression of multiple myeloma-related subgroups on metal ion-related pathways to explore the relationship between MM and metal ions.

Methods

We performed a fair examination using single-cell RNA sequencing on 32 bone marrow specimens collected from 22 individuals at different points of MM advancement and 9 individuals without any health issues. To analyze the scRNA-seq data, we employed advanced computational algorithms, including Slingshot, Monocle2, and other methodologies. Specifically, Slingshot and Monocle2 enabled us to simulate the biological functionalities of different cell populations and map trajectories of cell developmental pathways. Additionally, we utilized the UMAP algorithm, a powerful dimension reduction technique, to cluster cells and identify genes that were differentially expressed across clusters.

Results

Our study revealed distinct gene expression patterns and molecular pathways within each patient, which exhibited associations with disease progression. The analysis provided insights into the tumor microenvironment (TME), intra- and inter-patient heterogeneity, and cell-cell interactions mediated by ligand-receptor signaling. And found that multiple myeloma-related subgroups were expressed higher levels in MMP and TIMP pathways, there were some associations.

Conclusion

Our study presents a fresh perspective for future research endeavors and clinical interventions in the field of MM. The identified gene expression patterns and molecular pathways hold immense potential as therapeutic targets for the treatment of multiple myeloma. The utilization of scRNA-seq technology has significantly contributed to a more precise understanding of the complex cellular processes and interactions within MM. Through these advancements, we are now better equipped to unravel the underlying mechanisms driving the development and progression of this complex disease.

Elevated CDC45 Expression Predicts Poorer Overall Survival Prognoses and Worse Immune Responses for Kidney Renal Clear Cell Carcinoma via Single-Cell and Bulk RNA-Sequencing

The main objective of this paper is to analyze the prognostic and immunological value of CDC45 in kidney renal clear cell carcinoma (KIRC) using single-cell and bulk RNA-sequencing approaches. The expression of CDC45 in KIRC was evaluated by the HPA database, the TCGA-KIRC dataset and verified by PCR analysis and single-cell RNA-sequencing. The ability of CDC45 to independently predict prognosis in KIRC was confirmed by univariate/multivariate regression analysis. Gene set enrichment analysis (GSEA) was employed to explore CDC45-related pathways in KIRC. In addition, Relationships between CDC45 and immunity were also examined. Elevated CDC45 expression in KIRC was demonstrated at mRNA and protein levels. The results of the correlation analysis showed that as CDC45 expression increased, so did the histological grade, clinical stage, and TNM stage of the patients (p < 0.05). Univariate/multivariate regression analysis suggested CDC45 as an independent prognostic factor for KIRC. Seven pathways related to CDC45 were screened through GSEA. Meanwhile, we found that CDC45 was correlated with tumor mutational burden (TMB) and microsatellite instability (MSI) but not tumor neoantigen burden (TNB). Regarding immunity, CDC45 exhibited correlations with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Besides, low CDC45 expression was shown to be associated with a better response to immunotherapy. Single-cell RNA-sequencing revealed that CDC45 was differently expressed in T cells (p < 0.05). CDC45 showed potential as a prognostic biomarker and therapeutic target for KIRC. Meanwhile, the CDC45 low expression group was more sensitive to immunotherapy.

SLAMF8 can predict prognosis of pan-cancer and the immunotherapy response effectivity of gastric cancer

SLAMF8, the eighth member of the Signaling Lymphocytic Activation Molecule Family (SLAMF), functions in the regulation of the development and activity of diverse immune cells as a costimulatory receptor within the SLAMF family. Studies had revealed that SLAMF8 is expressed higher in several autoimmune inflammation diseases and tumors. Nevertheless, the connection between SLAMF8 and pan-cancer remains undisclosed. The research investigated the correlation between SLAMF8 and various factors including the immune microenvironment, microsatellite instability, immune novel antigen, gene mutation, immune regulatory factors, immune blockade TMB, and immune or molecular subtypes of SLAMF8 in verse cancer types. Immunohistochemistry was ultimately employed to validate the presence of the SLAMF8 gene in various tumor types including hepatocellular carcinoma, prostate adenocarcinoma, and kidney renal clear cell carcinoma. Furthermore, the relationship between SLAMF8 expression and the therapeutic efficacy of the PD1 blockade agent, Sintilimab, treatment in gastric cancer was validated. The result of differential analysis suggested that SLAMF8 was over-expressed in pan-cancer compared with paracancerous tissues. The analysis of survival indicated a connection between SLAMF8 and the overall prognosis in different types of cancers, where higher levels of SLAMF8 were found to be significantly linked to unfavorable outcomes in patients but favorable outcome of immunotherapy in gastric cancer. Significant correlations were observed between SLAMF8 levels and pan-cancer tumorigenesis, tumor metabolism, and immunity. As a result, SLAMF8 may become an important prognostic biomarker in the majority of tumors and a hopeful gene target for immunotherapy against gastric cancer.

Comprehensive analysis and prognostic assessment of senescence-associated genes in bladder cancer

BACKGROUND

The prevalence and mortality of bladder cancer (BLCA) present a significant medical challenge. While the function of senescence-related genes in tumor development is recognized, their prognostic significance in BLCA has not been thoroughly explored.

METHODS

BLCA transcriptome datasets were sourced from the TCGA and GEO repositories. Gene groupings were determined through differential gene expression and non-negative matrix factorization (NMF) methodologies. Key senescence-linked genes were isolated using singular and multivariate Cox regression analyses, combined with lasso regression. Validation was undertaken with GEO database information. Predictive models, or nomograms, were developed by merging risk metrics with clinical records, and their efficacy was gauged using ROC curve methodologies. The immune response's dependency on the risk metric was assessed through the immune phenomenon score (IPS). Additionally, we estimated IC50 metrics for potential chemotherapeutic agents.

RESULTS

Reviewing 406 neoplastic and 19 standard tissue specimens from the TCGA repository facilitated the bifurcation of subjects into two unique clusters (C1 and C2) according to senescence-related gene expression. After a stringent statistical evaluation, a set of ten pivotal genes was discerned and applied for risk stratification. Validity tests for the devised nomograms in forecasting 1, 3, and 5-year survival probabilities for BLCA patients were executed via ROC and calibration plots. IC50 estimations highlighted a heightened responsiveness in the low-risk category to agents like cisplatin, cyclopamine, and sorafenib.

CONCLUSIONS

In summation, our research emphasizes the prospective utility of risk assessments rooted in senescence-related gene signatures for enhancing BLCA clinical oversight.

Prognostic model development and molecular subtypes identification in bladder urothelial cancer by oxidative stress signatures

BACKGROUND

Mounting studies indicate that oxidative stress (OS) significantly contributes to tumor progression. Our study focused on bladder urothelial cancer (BLCA), an escalating malignancy worldwide that is growing rapidly. Our objective was to verify the predictive precision of genes associated with overall survival (OS) by constructing a model that forecasts outcomes for bladder cancer and evaluates the prognostic importance of these genetic markers.

METHODS

Transcriptomic data were obtained from TCGA-BLCA and GSE31684, which are components of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. To delineate distinct molecular subtypes, we employed the non-negative matrix factorization (NMF)method. The significance of OS-associated genes in predicting outcomes was assessed using lasso regression, multivariate Cox analysis, and univariate Cox regression analysis. For external validation, we employed the GSE31684 dataset. CIBERSORT was utilized to examine the tumor immune microenvironment (TIME). A nomogram was created and verified using calibration and receiver operating characteristic (ROC) curves, which are based on risk signatures. We examined variations in clinical characteristics and tumor mutational burden (TMB) among groups classified as high-risk and low-risk. To evaluate the potential of immunotherapy, the immune phenomenon score (IPS) was computed based on the risk score. In the end, the pRRophetic algorithm was employed to forecast the IC50 values of chemotherapy medications.

RESULTS

In our research, we examined the expression of 275 genes associated with OS in 19 healthy and 414 cancerous tissues of the bladder obtained from the TCGA database. As a result, a new risk signature was created that includes 4 genes associated with OS (RBPMS, CRYAB, P4HB, and PDGFRA). We found two separate groups, C1 and C2, that showed notable variations in immune cells and stromal score. According to the Kaplan-Meier analysis, patients classified as high-risk experienced a considerably reduced overall survival in comparison to those categorized as low-risk (P<0.001). The predictive capability of the model was indicated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve surpassing 0.6. Our model showed consistent distribution of samples from both the GEO database and TCGA data. Both the univariate and multivariate Cox regression analyses validated the importance of the risk score in relation to overall survival (P < 0.001). According to our research, patients with a lower risk profile may experience greater advantages from using a CTLA4 inhibitor, whereas patients with a higher risk profile demonstrated a higher level of responsiveness to Paclitaxel and Cisplatin. In addition, methotrexate exhibited a more positive outcome in patients with low risk compared to those with high risk.

CONCLUSIONS

Our research introduces a novel model associated with OS gene signature in bladder cancer, which uncovers unique survival results. This model can assist in tailoring personalized treatment approaches and enhancing patient therapeutic effect in the management of bladder cancer.

Comprehending the cuproptosis and cancer-immunity cycle network: delving into the immune landscape and its predictive role in breast cancer immunotherapy responses and clinical endpoints

Background

The role of cuproptosis, a phenomenon associated with tumor metabolism and immunological identification, remains underexplored, particularly in relation to the cancer-immunity cycle (CIC) network. This study aims to rigorously examine the impact of the cuproptosis-CIC nexus on immune reactions and prognostic outcomes in patients with breast cancer (BC), striving to establish a comprehensive prognostic model.

Methods

In the study, we segregated data obtained from TCGA, GEO, and ICGC using CICs retrieved from the TIP database. We constructed a genetic prognostic framework using the LASSO-Cox model, followed by its validation through Cox proportional hazards regression. This framework's validity was further confirmed with data from ICGC and GEO. Explorations of the tumor microenvironment were carried out through the application of ESTIMATE and CIBERSORT algorithms, as well as machine learning techniques, to identify potential treatment strategies. Single-cell sequencing methods were utilized to delineate the spatial distribution of key genes within the various cell types in the tumor milieu. To explore the critical role of the identified CICs, experiments were conducted focusing on cell survival and migration abilities.

Results

In our research, we identified a set of 4 crucial cuproptosis-CICs that have a profound impact on patient longevity and their response to immunotherapy. By leveraging these identified CICs, we constructed a predictive model that efficiently estimates patient prognoses. Detailed analyses at the single-cell level showed that the significance of CICs. Experimental approaches, including CCK-8, Transwell, and wound healing assays, revealed that the protein HSPA9 restricts the growth and movement of breast cancer cells. Furthermore, our studies using immunofluorescence techniques demonstrated that suppressing HSPA9 leads to a notable increase in ceramide levels.

Conclusion

This research outlines a network of cuproptosis-CICs and constructs a predictive nomogram. Our model holds great promise for healthcare professionals to personalize treatment approaches for individuals with breast cancer. The work provides insights into the complex relationship between the cuproptosis-CIC network and the cancer immune microenvironment, setting the stage for novel approaches to cancer immunotherapy. By focusing on the essential gene HSPA9 within the cancer-immunity cycle, this strategy has the potential to significantly improve the efficacy of treatments against breast cancer.

Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers a unique form of programmed cell death known as disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced cell death, providing novel insights into immunotherapeutic response and prognostic assessment in patients with colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis and Lasso regression analysis were performed to identify differentially expressed genes strongly associated with prognosis. Subsequently, a multifactorial model for prognostic risk assessment was developed using multiple Cox proportional hazard regression. Furthermore, we conducted comprehensive evaluations of the characteristics of disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognosis-related genes in COAD patients were validated using quantitative real-time fluorescence PCR (qRT-PCR). Additionally, the role of ZEB1-SA1 in colon cancer was investigated through CCK8 assays, wound healing experiment and transwell experiments. Results: disulfidptosis response-related LncRNAs were identified as robust predictors of COAD prognosis. Multifactorial analysis revealed that the risk score derived from these LncRNAs served as an independent prognostic factor for COAD. Patients in the low-risk group exhibited superior overall survival (OS) compared to those in the high-risk group. Accordingly, our developed Nomogram prediction model, integrating clinical characteristics and risk scores, demonstrated excellent prognostic efficacy. In vitro experiments demonstrated that ZEB1-SA1 promoted the proliferation and migration of COAD cells. Conclusion: Leveraging medical big data and artificial intelligence, we constructed a prediction model for disulfidptosis response-related LncRNAs based on the TCGA-COAD cohort, enabling accurate prognostic prediction in colon cancer patients. The implementation of this model in clinical practice can facilitate precise classification of COAD patients, identification of specific subgroups more likely to respond favorably to immunotherapy and chemotherapy, and inform the development of personalized treatment strategies for COAD patients based on scientific evidence.

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.

Astragaloside IV suppresses the migration and EMT progression of cervical cancer cells by inhibiting macrophage M2 polarization through TGFβ/Smad2/3 signaling

Cervical cancer (CC) is a gynecological malignant tumor worldwide. Astragaloside IV (AS-IV) has been found to exert antitumor effects on CC. In addition, M2-polarized macrophages, known as tumor-associated macrophages (TAMs), play an important role in promoting cancer cell growth and angiogenesis. Thus, we explored the association between the antitumor effect of AS-IV and macrophage polarization in CC. Flow cytometry, ELISA, and RT‒qPCR assays were applied to detect the levels of CD163, IL-10, TGFβ, and CD206 in M2 macrophages with or without AS-IV treatment. In addition, conditioned medium (CM) was collected from these M2 macrophages, and CC cells were then cultured in various CMs. Wound healing and transwell assays were used to assess the migratory ability of CC cells. In this study, we found that AS-IV significantly inhibited M2 polarization of macrophages, as shown by decreased CD163, IL-10, TGFβ, and CD206 expression. In addition, compared with CM from M2 macrophages, CM from AS-IV-treated M2 macrophages notably inhibited angiogenesis, migration, and epithelial-mesenchymal transition (EMT) in CC cells. Furthermore, compared with CM from M2 macrophages, CM from AS-IV-treated M2 macrophages markedly reduced p-Smad2 and p-Smad3 protein expression in CC cells, and these changes were reversed by TGF-β treatment. Collectively, suppression of M2-like polarization of macrophages by AS-IV could prevent the migration and EMT of CC cells by inactivating TGF-β/Smad2/3 signaling. These findings might provide some theoretical support for exploring novel treatments for CC.