Programmed Cell Death Ligand-1 (PD-L1) and CD8 Expression Profiling Identify an Immunologic Subtype of Pancreatic Ductal Adenocarcinomas with Favorable Survival

Comprehensive pan-cancer analysis of ZNF337 as a potential diagnostic, immunological, and prognostic biomarker

BACKGROUND

Zinc Finger Protein 337 (ZNF337) is a novel Zinc Finger (ZNF) protein family member. However, the roles of ZNF337 in human cancers have not yet been investigated.

METHODS

In this study, with the aid of TCGA databases, GTEx databases, and online websites, we determined the expression levels of ZNF337 in pan-cancer and its potential value as a diagnostic and prognostic marker for pan-cancer and analyzed the relationship between ZNF337 expression and immune cell infiltration and immune checkpoint genes. We then focused our research on the potential of ZNF337 as a biomarker for diagnostic and prognostic in KIRC (kidney renal clear cell carcinoma) and validated in the E-MTAB-1980 database. Moreover, the expression of ZNF337 was detected through qRT-PCR and Western blotting (WB). CCK-8 experiment, colony formation experiment, and EDU experiment were performed to evaluate cell proliferation ability. Wound healing assay and transwell assay were used to analyze its migration ability. The qRT-PCR and WB were used to detect the expression of ZNF337 in tumor tissues and paracancerous tissues of KIRC patients.

RESULTS

The pan-cancer analysis revealed that abnormal ZNF337 expression was found in multiple human cancer types. ZNF337 had a high diagnostic value in pan-cancer and a significant association with the prognosis of certain cancers, indicating that ZNF337 may be a valuable prognostic biomarker for multiple cancers. Further analysis demonstrated that the expression level of ZNF337 displayed significant correlations with cancer-associated fibroblasts, immune cell infiltration, and immune checkpoint genes in many tumors. Additionally, ZNF337 was observed to have a high expression in KIRC. Its expression was significantly associated with poor prognosis [overall survival (OS), disease-specific survival (DSS)], age, TNM stage, histologic grade, and pathologic stage. The high ZNF337 expression was associated with poor prognosis in the E-MTAB-1980 validation cohort. The in vitro experiments suggested that the expression of ZNF337 in KIRC tumor tissues was higher than in adjacent tissues, and ZNF337 knockdown inhibited the proliferation and migration of KIRC cells, whereas overexpression of ZNF337 had the opposite effects.

CONCLUSIONS

ZNF337 might be an important prognostic and immunotherapeutic biomarker for pan-cancer, especially in KIRC.

SLC4A4 as a novel biomarker involved in immune system response and lung adenocarcinoma progression

BACKGROUND

Altered expression and activity of solute carrier family 4 member 4 (SLC4A4) could affect the growth, survival and metastasis of tumor cells. Currently, the role of SLC4A4 in lung adenocarcinoma (LUAD) immunotherapy and prognosis was not entirely clear.

METHODS

We analyzed SLC4A4 expression in LUAD tissues and cell lines using quantitative reverse transcription-polymerase chain reaction, Western blotting, and immunohistochemistry. The effects of SLC4A4 overexpression on angiogenesis, cell migration, invasion, and epithelial-mesenchymal transition were examined. Public databases helped construct a risk model evaluating SLC4A4's expression on LUAD prognosis and immunotherapy response. Additionally, a xenograft model, flow cytometry, and enzyme-linked immunosorbent assay further explored SLC4A4's role in tumor immune microenvironment infiltration.

RESULTS

Upregulation of SLC4A4 promoted apoptosis in the LUAD cell line and significantly inhibited the migration and invasive ability of cancer cells (P<0.01). A total of 10 key genes (including SIGLEC6, RHOV, PIR, MOB3B, MIR3135B, LPAR6, KRT8, ITGA2, CPS1, and C6) were screened according to SLC4A4 expression, immune score and stromal score, and a prognostic model with good outcome was constructed (AUC values of which in the training cohort at 1,3, and 5 years reached 0.73, 0.73, and 0.72, respectively). Importantly, we demonstrated that high expression of SLC4A4 was able to increase the proliferation level and cytokine secretion of CD8+ T cells for the purpose of promoting the immune system response to LUAD.

CONCLUSION

Our study revealed that SLC4A4 can serve as a prognostic indicator for LUAD, providing new insights into the treatment and diagnosis of LUAD.

First-line penpulimab (an anti-PD1 antibody) and anlotinib (an angiogenesis inhibitor) with nab-paclitaxel/gemcitabine (PAAG) in metastatic pancreatic cancer: a prospective, multicentre, biomolecular exploratory, phase II trial

Metastatic pancreatic cancer (mPC) has a dismal prognosis. Herein, we conducted a prospective, multicentre, single-arm, phase II trial evaluating the efficacy and safety of penpulimab and anlotinib in combination with nab-paclitaxel/gemcitabine (PAAG) in patients with first-line mPC (NCT05493995). The primary endpoints included the objective response rate (ORR) and disease control rate (DCR), while secondary endpoints encompassed progression-free survival (PFS), overall survival (OS), and safety. In 66 patients analysed for efficacy, the best response, indicated by the ORR, was recorded at 50.0% (33/66) (95% CI, 37.4-62.6%), with 33 patients achieving partial response (PR). Notably, the DCR was 95.5% (63/66, 95% CI, 87.3-99.1%). The median PFS (mPFS) and OS (mOS) were 8.8 (95% CI, 8.1-11.6), and 13.7 (95% CI, 12.4 to not reached) months, respectively. Grade 3/4 treatment-related adverse events (TRAEs) were reported in 39.4% of patients (26/66). In prespecified exploratory analysis, patients with altered SWI/SNF complex had a poorer PFS. Additionally, low serum CA724 level, high T-cell recruitment, low Th17 cell recruitment, and high NK CD56dim cell scores at baseline were potential predicative biomarkers for more favourable efficacy. In conclusion, PAAG as a first-line therapy demonstrated tolerability with promising clinical efficacy for mPC. The biomolecular findings identified in this study possess the potential to guide the precise clinical application of the triple-combo regimen.

Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study

Pancreatic adenocarcinoma characterized by a mere 10% 5-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

Inflammation-based lung adenocarcinoma molecular subtype identification and construction of an inflammation-related signature with bulk and single-cell RNA-seq data

The role of inflammation is increasingly understood to have a central influence on therapeutic outcomes and prognosis in lung adenocarcinoma (LUAD). However, the detailed molecular divisions involved in inflammatory responses are yet to be fully elucidated. Our study identified two main inflammation-oriented LUAD grades: the inflammation-low (INF-low) and the inflammation-high (INF-high) subtypes. Both presented with unique clinicopathological features, implications for prognosis, and distinctive tumor microenvironment profiles. Broadly, the INF-low grade, marked by its dominant immunosuppressive tumor microenvironment, was accompanied by less favorable prognostic outcomes and a heightened prevalence of oncogenic mutations. In contrast, the INF-high grade exhibited more optimistic clinical trajectories, underscored by its immune-active environment. In addition, our efforts led to the conceptualization and empirical validation of an inflammation-centric predictive model with considerable predictive potency. Our study paves the way for a refined inflammation-centric LUAD classification and fosters a deeper understanding of tumor microenvironment intricacies.

Xevinapant Combined with Pembrolizumab in Patients with Advanced, Pretreated, Colorectal and Pancreatic Cancer: Results of the Phase Ib/II CATRIPCA Trial

PURPOSE

Xevinapant is an orally available inhibitor of apoptosis proteins (IAP) inhibitor. Preclinical data suggest that IAP antagonism may synergize with immune checkpoint blockers by modulating the NFκB pathway in immune cells.

PATIENTS AND METHODS

Adult patients with non-high microsatellite instability advanced/metastatic pancreatic ductal adenocarcinoma (PDAC) or colorectal cancer were enrolled in this phase Ib/II study and received pembrolizumab 200 mg every 3 weeks intravenously, and ascending doses of oral xevinapant (100, 150, and 200 mg daily for 14 days on/7 days off). Dose escalation followed a 3+3 design with a 21-day dose-limiting toxicity (DLT) evaluation period. Following the determination of the recommended phase II dose (RP2D), 14 patients with PDAC and 14 patients with colorectal cancer were enrolled in expansion cohorts to assess preliminary efficacy.

RESULTS

Forty-one patients (26 males) with a median age of 64 years were enrolled: 13 in the dose escalation and 28 in the two expansion cohorts. No DLT was observed during dose escalation. The RP2D was identified as xevinapant 200 mg/day + pembrolizumab 200 mg every 3 weeks. The most common adverse events (AE) were fatigue (37%), gastrointestinal AE (decreased appetite in 37%, nausea in 24%, stomatitis in 12%, and diarrhea and vomiting in 10% each), and cutaneous AE (pruritus, dry skin, and rash seen in 20%, 15%, and 15% of patients, respectively). The best overall response according to RECIST1.1 was partial response (confirmed) in 1 (3%), stable disease in 4 (10%), and progressive disease in 35 (88%).

CONCLUSIONS

Xevinapant combined with pembrolizumab was well tolerated with no unexpected AEs. However, antitumor activity was low.

Prognostic and immunotherapeutic implications of bilirubin metabolism-associated genes in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a major subtype of non-small-cell lung cancer and accompanies high mortality rates. While the role of bilirubin metabolism in cancer is recognized, its specific impact on LUAD and patient response to immunotherapy needs to be elucidated. This study aimed to develop a prognostic signature of bilirubin metabolism-associated genes (BMAGs) to predict outcomes and efficacy of immunotherapy in LUAD. We analysed gene expression data from The Cancer Genome Atlas (TCGA) to identify survival-related BMAGs and construct a prognostic model in LUAD. The prognostic efficacy of our model was corroborated by employing TCGA-LUAD and five Gene Expression Omnibus datasets, effectively stratifying patients into risk-defined cohorts with marked disparities in survival. The BMAG signature was indeed an independent prognostic determinant, outperforming established clinical parameters. The low-risk group exhibited a more favourable response to immunotherapy, highlighted by increased immune checkpoint expression and immune cell infiltration. Further, somatic mutation profiling differentiated the molecular landscapes of the risk categories. Our screening further identified potential drug candidates preferentially targeting the high-risk group. Our analysis of critical BMAGs showed the tumour-suppressive role of FBP1, highlighting its suppression in LUAD and its inhibitory effects on tumour proliferation, migration and invasion, in addition to its involvement in cell cycle and apoptosis regulation. These findings introduce a potent BMAG-based prognostic indicator and offer valuable insights for prognostication and tailored immunotherapy in LUAD.

A prognostic model for hepatocellular carcinoma patients based on polyunsaturated fatty acid-related genes

OBJECTIVE

Polyunsaturated fatty acids (PUFAs) have attracted increasing attention for their role in liver cancer development. The objective of this study is to develop a prognosis prediction model for patients with liver cancer based on PUFA-related metabolic gene characteristics.

METHOD

Transcriptome data and clinical data were obtained from public databases, while gene sets related to PUFAs were acquired from the gene set enrichment analysis (GSEA) database. Univariate Cox analysis was conducted on the training set, followed by LASSO logistic regression and multivariate Cox analysis on genes with p < .05. Subsequently, the stepwise Akaike information criterion method was employed to construct the model. The high- and low-risk groups were divided based on the median score, and the model's survival prediction ability, diagnostic efficiency, and risk score distribution of clinical features were validated. The above procedures were also validated in the validation set. Immune infiltration levels were evaluated using four algorithms, and the immunotherapeutic potential of different groups was explored. Significant enrichment pathways among different groups were selected based on the GSEA algorithm, and mutation analyses were conducted. Nomogram prognostic models were constructed by incorporating clinical factors and risk scores using univariate and multivariate Cox regression analysis, validated through calibration curves and clinical decision curves. Additionally, sensitivity analysis of drugs was performed to screen potential targeted drugs.

RESULTS

We constructed a prognostic model comprising eight genes (PLA2G12A, CYP2C8, ABCCI, CD74, CCR7, P2RY4, P2RY6, and YY1). Validation across multiple datasets indicated the model's favorable prognostic prediction ability and diagnostic efficiency, with poorer grading and staging observed in the high-risk group. Variations in mutation status and pathway enrichment were noted among different groups. Incorporating Stage, Grade, T.Stage, and RiskScore into the nomogram prognostic model demonstrated good accuracy and clinical decision benefits. Multiple immune analyses suggested greater benefits from immunotherapy in the low-risk group. We predicted multiple targeted drugs, providing a basis for drug development.

CONCLUSION

Our study's multifactorial prognostic model across multiple datasets demonstrates good applicability, offering a reliable tool for personalized therapy. Immunological and mutation-related analyses provide theoretical foundations for further research. Drug predictions offer important insights for future drug development and treatment strategies. Overall, this study provides comprehensive insights into tumor prognosis assessment and personalized treatment planning.

Machine learning unveils immune-related signature in multicenter glioma studies

In glioma molecular subtyping, existing biomarkers are limited, prompting the development of new ones. We present a multicenter study-derived consensus immune-related and prognostic gene signature (CIPS) using an optimal risk score model and 101 algorithms. CIPS, an independent risk factor, showed stable and powerful predictive performance for overall and progression-free survival, surpassing traditional clinical variables. The risk score correlated significantly with the immune microenvironment, indicating potential sensitivity to immunotherapy. High-risk groups exhibited distinct chemotherapy drug sensitivity. Seven signature genes, including IGFBP2 and TNFRSF12A, were validated by qRT-PCR, with higher expression in tumors and prognostic relevance. TNFRSF12A, upregulated in GBM, demonstrated inhibitory effects on glioma cell proliferation, migration, and invasion. CIPS emerges as a robust tool for enhancing individual glioma patient outcomes, while IGFBP2 and TNFRSF12A pose as promising tumor markers and therapeutic targets.

Pan-cancer analysis of the prognostic and immunological role of FKBP4

Objectives

Our previous studies revealed the significant roles of FK506-binding protein 4 (FKBP4) in tumorigenesis, however, there has been no pan-cancer analysis of FKBP4. Using bioinformatics, the current study reported the expression and prognostic role of FKBP4, and the correlation between FKBP4 and clinicopathological parameters, methylation, molecular network, immunological traits and drug sensitivity.

Methods

RNA sequencing data, somatic mutation, and related clinical information were obtained from TCGA using UCSC Xena. The association between FKBP4 expression and clinical features was assessed using TISIDB. The relationships between FKBP4 expression and tumour stage, OS, DSS, DFS, and PFS were analysed using univariate cox regression analysis. The radar plots for TMB and MSI were obtained using "Fmsb" R package. UALCAN was used to explore the effect of FKBP4 methylation on tumour and normal samples. CBioportal was used to analyse copy number mutations in FKBP4 Gene expression and drug sensitivity data were downloaded from the CellMiner database. GO analysis was performed for the high and the low expression of FKBP4 compared with the median level of FKBP4 using clusterProfiler4.0.

Results

FKBP4 expression is significantly upregulated in various types of cancers. Cox regression analysis showed that high FKBP4 levels were correlated with poor OS, DSS, DFS, and PFS in most patients with cancer. Methylation of FKBP4 DNA was upregulated in most cancers, and FKBP4 expression is positively associated with transmethylase expression. FKBP4 and its copy were significantly associated with the expression of immune-infiltrating cells, immune checkpoint genes, immune modulators, TMB, MMR, and MSI. FKBP4 expression levels significantly correlated with 16 different drug sensitivities (all p < 0.05).

Conclusions

Our pan-cancer bioinformatic analysis revealed a potential mechanism underlying the effects of FKBP4 on the prognosis and progression of various cancers.

A TGF-β signaling-related lncRNA signature for prediction of glioma prognosis, immune microenvironment, and immunotherapy response

AIMS

The dysregulation of TGF-β signaling is a crucial pathophysiological process in tumorigenesis and progression. LncRNAs have diverse biological functions and are significant participants in the regulation of tumor signaling pathways. However, the clinical value of lncRNAs related to TGF-β signaling in glioma is currently unclear.

METHODS

Data on glioma's RNA-seq transcriptome, somatic mutation, DNA methylation data, and clinicopathological information were derived from the CGGA and TCGA databases. A prognostic lncRNA signature was constructed by Cox and LASSO regression analyses. TIMER2.0 database was utilized to deduce immune infiltration characteristics. "ELMER v.2" was used to reconstruct TF-methylation-gene regulatory network. Immunotherapy and chemotherapy response predictions were implemented by the TIDE algorithm and GDSC database, respectively. In vitro and in vivo experiments were conducted to verify the results and clarify the regulatory mechanism of lncRNA.

RESULTS

In glioma, a TGF-β signaling-related 15-lncRNA signature was constructed, including AC010173.1, HOXA-AS2, AC074286.1, AL592424.1, DRAIC, HOXC13-AS, AC007938.1, AC010729.1, AC013472.3, AC093895.1, AC131097.4, AL606970.4, HOXC-AS1, AGAP2-AS1, and AC002456.1. This signature proved to be a reliable prognostic tool, with high risk indicating an unfavorable prognosis and being linked to malignant clinicopathological and genomic mutation traits. Risk levels were associated with different immune infiltration landscapes, where high risk was indicative of high levels of macrophage infiltration. In addition, high risk also suggested better immunotherapy and chemotherapy response. cg05987823 was an important methylation site in glioma progression, and AP-1 transcription factor family participated in the regulation of signature lncRNA expression. AGAP2-AS1 knockdown in in vitro and in vivo experiments inhibited the proliferation, migration, and invasion of glioma cells, as well as the growth of glioma, by downregulating the expression levels of NF-κB and ERK 1/2 in the TGF-β signaling pathway.

CONCLUSIONS

A prognostic lncRNA signature of TGF-β signaling was established in glioma, which can be used for prognostic judgment, immune infiltration status inference, and immunotherapy response prediction. AGAP2-AS1 plays an important role in glioma progression.

Identification and validation of the clinical prediction model and biomarkers based on chromatin regulators in colon cancer by integrated analysis of bulk- and single-cell RNA sequencing data

Background

Chromatin regulators (CRs) are implicated in the development of cancer, but a comprehensive investigation of their role in colon adenocarcinoma (COAD) is inadequate. The purpose of this study is to find CRs that can provide recommendations for clinical diagnosis and treatment, and to explore the reasons why they serve as critical CRs.

Methods

We obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted Gene Co-Expression Network Analysis (WGCNA) screened tumor-associated CRs. LASSO-Cox regression was used to construct the model and to screen key CRs together with support vector machine (SVM), the univariate Cox regression. We used single-cell data to explore the expression of CRs in cells and their communication. Immune infiltration, immune checkpoints, mutation, methylation, and drug sensitivity analyses were performed. Gene expression was verified by quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Pan-cancer analysis was used to explore the importance of hub CRs.

Results

We finally obtained 32 tumor-associated CRs. The prognostic model was constructed based on RCOR2, PPARGC1A, PKM, RAC3, PHF19, MYBBP1A, ORC1, and EYA2 by the LASSO-Cox regression. Single-cell data revealed that the model was immune-related. Combined with immune infiltration analysis, immune checkpoint analysis, and tumor immune dysfunction and exclusion (TIDE) analysis, the low-score risk group had more immune cell infiltration and better immune response. Mutation and methylation analysis showed that multiple CRs may be mutated and methylated in colon cancer. Drug sensitivity analysis revealed that the low-risk group may be more sensitive to several drugs and PKM was associated with multiple drugs. Combined with machine learning, PKM is perhaps the most critical gene in CRs. Pan-cancer analysis showed that PKM plays a role in the prognosis of cancers.

Conclusions

We developed a prognostic model for COAD based on CRs. Increased expression of the core gene PKM is linked with a poor prognosis in several malignancies.

Comprehensive analysis of co-expressed genes with TDP-43: prognostic and therapeutic potential in lung adenocarcinoma

BACKGROUND

Transactivating DNA-binding protein 43 (TDP-43) is intimately associated with tumorigenesis and progression by regulating mRNA splicing, transport, stability, and non-coding RNA molecules. The exact role of TDP-43 in lung adenocarcinoma (LUAD) has not yet been fully elucidated, despite extensive research on its function in various cancer types. An imperative aspect of comprehending the underlying biological characteristics associated with TDP-43 involves investigating the genes that are co-expressed with this protein. This study assesses the prognostic significance of these co-expressed genes in LUAD and subsequently explores potential therapeutic strategies based on these findings.

METHODS

Transcriptomic and clinical data pertaining to LUAD were retrieved from open-access databases to establish an association between mRNA expression profiles and the presence of TDP-43. A risk-prognosis model was developed to compare patient survival rates across various groups, and its accuracy was also assessed. Additionally, differences in tumor stemness, mutational profiles, tumor microenvironment (TME) characteristics, immune checkpoints, and immune cell infiltration were analyzed in the different groups. Moreover, the study entailed predicting the potential response to immunotherapy as well as the sensitivity to commonly employed chemotherapeutic agents and targeted drugs for each distinct group.

RESULTS

The TDP-43 Co-expressed Gene Risk Score (TCGRS) model was constructed utilizing four genes: Kinesin Family Member 20A (KIF20A), WD Repeat Domain 4 (WDR4), Proline Rich 11 (PRR11), and Glia Maturation Factor Gamma (GMFG). The value of this model in predicting LUAD patient survival is effectively illustrated by both the Kaplan-Meier (K-M) survival curve and the area under the receiver operating characteristic curve (AUC-ROC). The Gene Set Enrichment Analysis (GSEA) revealed that the high TCGRS group was primarily enriched in biological pathways and functions linked to DNA replication and cell cycle; the low TCGRS group showed primary enrichment in immune-related pathways and functions. The high and low TCGRS groups showed differences in tumor stemness, mutational burden, TME, immune infiltration level, and immune checkpoints. The predictions analysis of immunotherapy indicates that the Tumor Immune Dysfunction and Exclusion (TIDE) score (p < 0.001) and non-response rate (74% vs. 51%, p < 0.001) in the high TCGRS group are higher than those in the low TCGRS group. The Immune Phenotype Score (IPS) in the high TCGRS group is lower than in the low TCGRS group (p < 0.001). The drug sensitivity analysis revealed that the half-maximal inhibitory concentration (IC50) values for cisplatin, docetaxel, doxorubicin, etoposide, gemcitabine, paclitaxel, vincristine, erlotinib, and gefitinib (all p < 0.01) in the high TCGRS group are lower than those in the low TCGRS group.

CONCLUSIONS

The TCGRS derived from the model exhibits a reliable biomarker for evaluating both prognosis and treatment effectiveness among patients with LUAD. This study is anticipated to offer valuable insights into developing effective treatment strategies for this patient population. It is believed that this study is anticipated to contribute significantly to clinical diagnostics, the development of therapeutic drugs, and the enhancement of patient care.

The CSF-1R inhibitor pexidartinib affects FLT3-dependent DC differentiation and may antagonize durvalumab effect in patients with advanced cancers

Tumor-associated macrophages (TAMs) are a critical determinant of resistance to PD-1/PD-L1 blockade. This phase 1 study (MEDIPLEX, NCT02777710) investigated the safety and efficacy of pexidartinib, a CSF-1R-directed tyrosine kinase inhibitor (TKI), and durvalumab (anti-PD-L1) in patients with advanced colorectal and pancreatic carcinoma with the aim to enhance responses to PD-L1 blockade by eliminating CSF-1-dependent suppressive TAM. Forty-seven patients were enrolled. No unexpected toxicities were observed, one (2%) high microsatellite instability CRC patient had a partial response, and seven (15%) patients experienced stable disease as their best response. Increase of CSF-1 concentrations and decrease of CD14lowCD16high monocytes in peripheral blood mononuclear cells (PBMCs) confirmed CSF-1R engagement. Treatment decreased blood dendritic cell (DC) subsets and impaired IFN-λ/IL-29 production by type 1 conventional DCs in ex vivo TLR3-stimulated PBMCs. Pexidartinib also targets c-KIT and FLT3, both key growth factor receptors of DC development and maturation. In patients, FLT3-L concentrations increased with pexidartinib treatment, and AKT phosphorylation induced by FLT3-L ex vivo stimulation was abrogated by pexidartinib in human blood DC subsets. In addition, pexidartinib impaired the FLT3-L- but not GM-CSF-dependent generation of DC subsets from murine bone marrow (BM) progenitors in vitro and decreased DC frequency in BM and tumor-draining lymph node in vivo. Our results demonstrate that pexidartinib, through the inhibition of FLT3 signaling, has a deleterious effect on DC differentiation, which may explain the limited antitumor clinical activity observed in this study. This work suggests that inhibition of FLT3 should be considered when combining TKIs with immune checkpoint inhibitors.

Malignant cell receptor-ligand subtypes guide the prediction of prognosis and personalized immunotherapy of liver cancer

OBJECTIVE

Liver cancer is a prevalent disease with a dismal prognosis. The aim of the research is to identify subgroups based on malignant cell receptor ligand gene from single-cell RNA, which might lead to customized immunotherapy for patients with liver cancer.

METHODS

Based on scRNA-seq data, we identified the receptor-ligand genes associated with prognosis and classify patients into molecular subtypes by univariate Cox regression and consensus clustering. LASSO regression was performed to construct a prognostic model, which was validated in TCGA and ICGC datasets. Immune infiltration and prediction of immunotherapy response were analyzed using ssGSEA, ESTIMATE, TIDE, and TRS score calculation. Finally, qPCR and Western blot validation of key genes and protein levels in cell lines.

RESULTS

A risk model using 16-gene expression levels predicted liver cancer patients' prognosis. The RiskScore associated significantly with tumor clinical characteristics and immunity, integrated with clinicopathological features for survival prediction. Differential expression of SRXN1 was verified in hepatocellular carcinoma and normal liver cells.

CONCLUSION

Our study utilizes single-cell analysis to investigate the communication between malignant cells and other cell types, identifying molecular subtypes based on malignant cell receptor ligand genes, offering new insights for the development of personalized immunotherapy and prognostic prediction models.

Characterizing ligand-receptor interactions and unveiling the pro-tumorigenic role of CCL16-CCR1 axis in the microenvironment of hepatocellular carcinoma

Background

The heterogeneity of the tumor microenvironment significantly influences the prognosis of hepatocellular carcinoma (HCC) patients, with cell communication through ligand-receptor complexes playing a central role.

Methods

We conducted single-cell transcriptomic analysis on ten HCC tissues to identify ligand-receptor genes involved in malignant HCC cell communication using CellChat. Leveraging RNA-Seq data from the TCGA Liver Cancer (TCGA-LIHC) and Liver Cancer - RIKEN, JP (LIRI-JP) cohorts, we employed Cox regression analysis to screen for prognosis-related genes. Prognostic risk models were constructed through unsupervised clustering and differential gene expression analysis. Subsequently, a co-culture system involving tumor cells and macrophages was established. A series of experiments, including Transwell assays, immunofluorescence staining, immunoprecipitation, flow cytometry, and immunohistochemistry, were conducted to elucidate the mechanism through which HCC cells recruit macrophages via the CCL16-CCR1 axis.

Results

Single-cell analysis unveiled significant interactions between malignant HCC cells and macrophages, identifying 76 related ligand-receptor genes. Patients were classified into three subtypes based on the expression patterns of eight prognosis-related ligand-receptor genes. The subtype with the worst prognosis exhibited reduced infiltration of T cell-related immune cells, downregulation of immune checkpoint genes, and increased M2-like tumor-associated macrophage scores. In vitro experiments confirmed the pivotal role of the CCL16-CCR1 axis in the recruitment and M2 polarization of tumor-associated macrophages. Clinical samples demonstrated a significant association between CCL16 protein expression levels and advanced stage, lymph node metastasis, and distant metastasis. Immunohistochemistry and immunofluorescence staining further confirmed the correlation between CCL16 and CCR1, CD68, and CD206, as well as CD68+CCR1+ macrophage infiltration.

Conclusions

Our study identified molecular subtypes, a prognostic model, and immune microenvironment features based on ligand-receptor interactions in malignant HCC cell communication. Moreover, we revealed the pro-tumorigenic role of HCC cells in recruiting M2-like tumor-associated macrophages through the CCL16-CCR1 axis.

Tumor immune microenvironment-based therapies in pancreatic ductal adenocarcinoma: time to update the concept

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal solid tumors. The tumor immune microenvironment (TIME) formed by interactions among cancer cells, immune cells, cancer-associated fibroblasts (CAF), and extracellular matrix (ECM) components drives PDAC in a more immunosuppressive direction: this is a major cause of therapy resistance and poor prognosis. In recent years, research has advanced our understanding of the signaling mechanism by which TIME components interact with the tumor and the evolution of immunophenotyping. Through revolutionary technologies such as single-cell sequencing, we have gone from simply classifying PDACs as "cold" and "hot" to a more comprehensive approach of immunophenotyping that considers all the cells and matrix components. This is key to improving the clinical efficacy of PDAC treatments. In this review, we elaborate on various TIME components in PDAC, the signaling mechanisms underlying their interactions, and the latest research into PDAC immunophenotyping. A deep understanding of these network interactions will contribute to the effective combination of TIME-based therapeutic approaches, such as immune checkpoint inhibitors (ICI), adoptive cell therapy, therapies targeting myeloid cells, CAF reprogramming, and stromal normalization. By selecting the appropriate integrated therapies based on precise immunophenotyping, significant advances in the future treatment of PDAC are possible.

Systematic Analysis of Tumor Stem Cell-related Gene Characteristics to Predict the PD-L1 Immunotherapy and Prognosis of Gastric Cancer

AIMS

We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in gastric cancer (GC).

BACKGROUND

Tumor stemness is related to intratumoral heterogeneity, immunosuppression, and anti-tumor resistance. We developed a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.

OBJECTIVE

We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.

METHODS

We downloaded single-cell RNA sequencing (scRNA-seq) data of GC patients from the Gene-Expression Omnibus (GEO) database and screened GC stemness- related genes using CytoTRACE. We characterized the association of tumor stemness with immune checkpoint blockade (ICB) and immunity. Thereafter, a 9-stemness signature-based prognostic model was developed using weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis. The model predictive value was evaluated with a nomogram.

RESULTS

Early GC patients had significantly higher levels of stemness. The stemness score showed a negative relationship to tumor immune dysfunction and exclusion (TIDE) score and immune infiltration, especially T cells and B cells. A stemness-based signature based on 9 genes (ERCC6L, IQCC, NKAPD1, BLMH, SLC25A15, MRPL4, VPS35, SUMO3, and CINP) was constructed with good performance in prognosis prediction, and its robustness was validated in GSE26942 cohort. Additionally, nomogram and risk score exhibited the most powerful ability for prognosis prediction. High-risk patients exhibited a tendency to develop immune escape and low response to PD-L1 immunotherapy.

CONCLUSION

We developed a stemness-based gene signature for prognosis prediction with accuracy and reliability. This signature also helps clinical decision-making of immunotherapy for GC patients.

Construction of RNA Methylation Modification-immune-related lncRNA Molecular Subtypes and Prognostic Scoring System in Lung Adenocarcinoma

BACKGROUND

RNA methylation modification is not only intimately interrelated with cancer development and progression but also actively influences immune cell infiltration in the tumor microenvironment (TME). RNA methylation modification genes influence the therapeutic progression of lung adenocarcinoma (LUAD), and mining RNA methylation modification prognosis-related markers in LUAD is crucial for its precise prognosis.

METHODS

RNA-Seq data and Gene sets were collected from online databases or published literature. Genomic variation analysis was conducted by the Maftools package. RNA methylation-immune-related lncRNAs were obtained by Pearson correlation analysis. Then, Consistent clustering analysis was performed to obtain RNA methylation modification- immune molecular subtypes (RMM-I Molecular subtypes) in LUAD based on selected lncRNAs. COX and random survival forest analysis were carried out to construct the RMM-I Score. The receiver operating characteristic (ROC) curve and Kaplan Meier survival analysis were used to assess survival differences. Tumor immune microenvironment was assessed through related gene signatures and CIBERSORT algorithm. In addition, drug sensitivity analysis was executed by the pRRophetic package.

RESULTS

Four RNA methylation modified-immune molecular subtypes (RMM-I1, RMM- I2, RMM-I3, RMM-I4) were presented in LUAD. Patients in RMM-I4 exhibited excellent survival advantages and immune activity. HAVCR2, CD274, and CTLA-4 expression were activated in RMM-I4, which might be heat tumors and a potential beneficial group for immunotherapy. OGFRP1, LINC01116, DLGAP1-AS2, CRNDE, LINC01137, MIR210HG, and CYP1B1-AS1 comprised the RMM-I Score. The RMM-I Score exhibited excellent accuracy in the prognostic assessment of LUAD, as patients with a low RMM- I Score exhibited remarkable survival advantage. Patients with a low RMM-I score might be more sensitive to treatment with Docetaxel, Vinorelbine, Paclitaxel, Cisplatin, and immunotherapy.

CONCLUSION

The RMM-I molecular subtype constituted the novel molecular characteristic subtype of LUAD, which complemented the existing pathological typing. More refined and accurate molecular subtypes provide help to reveal the mechanism of LUAD development. In addition, the RMM-I score offers a reliable tool for accurate prognosis of LUAD.

Developing a RiskScore Model based on Angiogenesis-related lncRNAs for Colon Adenocarcinoma Prognostic Prediction

AIM

We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskScore model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment.

BACKGROUND

Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD.

OBJECTIVE

To identify key angiogenesis-related lncRNAs and build a RiskScore model to predict the survival probability of COAD patients and help optimize clinical treatment.

METHODS

Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskScore model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package.

RESULTS

Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskScore model. The high RiskScore group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskScore signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment.

CONCLUSION

We constructed a RiskScore model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.

Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations

Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.

Single-cell dissection reveals the role of aggrephagy patterns in tumor microenvironment components aiding predicting prognosis and immunotherapy on lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the leading malignant cancers. Aggrephagy plays a critical role in key genetic events for various cancers; yet, how aggrephagy functions within the tumor microenvironment (TME) in LUAD remains to be elucidated.

METHODS

In this study, by sequential non-negative matrix factorization (NMF) algorithm, pseudotime analysis, cell-cell interaction analysis, and SCENIC analysis, we have shown that aggrephagy genes demonstrated various patterns among different cell types in LUAD TME. LUAD and Immunotherapy cohorts from public repository were used to determine the prognosis and immune response of aggrephagy TME subtypes. The aggrephagy-deprived prognostic score (ADPS) was quantified based on machine learning algorithms.

RESULTS

The cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), and CD8+ T cells have various aggrephagy patterns, which enhance the intensity of intercellular communication and transcription factor activation. Furthermore, based on the signatures of the newly defined aggrephagy cell subtypes and expression profiles of large cohorts in LUAD patients, we determine that DYNC1I2+CAF-C1, DYNLL1+CAF-C2, PARK7+CAF-C3, VIM+Mac-C1, PARK7+Mac-C2, VIM+CD8+T_cells-C1, UBA52+CD8+T_cells-C2, TUBA4A+CD8+T_ cells-C3, and TUBA1A+CD8+T_cells-C4 are crucial prognostic factors for LUAD patients. The developed ADPS could predict survival outcomes and immunotherapeutic response across ten cohorts (n = 1838), and patients with low ADPS owned a better prognosis, lower genomic alterations, and are more sensitive to immunotherapy. Meanwhile, based on PRISM, CTRP, and CMAP databases, PLK inhibitor BI-2536, may be a potential agent for patients with high ADPS.

CONCLUSIONS

Taken together, our novel and systematic single-cell analysis has revealed the unique role of aggrephagy in remodeling the TME of LUAD. As a newly demonstrated biomarker, the ADPS facilitates the clinical management and individualized treatment of LUAD.

Construction of a 5-gene prognostic signature based on oxidative stress related genes for predicting prognosis in osteosarcoma

BACKGROUND

The understanding of the complex biological scenario of osteosarcoma will open the way to identifying new strategies for its treatment. Oxidative stress is a cancer-related biological scenario. At present, it is not clear the oxidative stress genes in affecting the prognosis and progression of osteosarcoma, the underlying mechanism as well as their impact on the classification of osteosarcoma subtypes.

METHODS

We selected samples and sequencing data from TARGET data set and GSE21257 data set, and downloaded oxidative stress related-genes (OSRGs) from MsigDB. Univariate Cox analysis of OSRG was conducted using TARGET data, and the prognostic OSRG was screened to conduct unsupervised clustering analysis to identify the molecular subtypes of osteosarcoma. Through least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression analysis of differentially expressed genes (DEGs) between subgroups, a risk assessment system for osteosarcoma was developed.

RESULTS

45 prognosis-related OSRGs genes were acquired, and two molecular subtypes of osteosarcoma were clustered. C2 cluster displayed prolonged overall survival (OS) accompanied with high degree of immune infiltration and enriched immune pathways. While cell cycle related pathways were enriched in C2 cluster. Based on DEGs between subgroups and Lasso analysis, 5 hub genes (ZYX, GJA5, GAL, GRAMD1B, and CKMT2) were screened to establish a robust prognostic risk model independent of clinicopathological features. High-risk group had more patients with cancer metastasis and death as well as C1 subtype with poor prognosis. Low-risk group exhibited favorable OS and high immune infiltration status. Additionally, the risk assessment system was optimized by building decision tree and nomogram.

CONCLUSIONS

This study defined two molecular subtypes of osteosarcoma with different prognosis and tumor immune microenvironment status based on the expression of OSRGs, and provided a new risk assessment system for the prognosis of osteosarcoma.

A robust six-gene prognostic signature based on two prognostic subtypes constructed by chromatin regulators is correlated with immunological features and therapeutic response in lung adenocarcinoma

Accumulating evidence has demonstrated that chromatin regulators (CRs) regulate immune cell infiltration and are correlated with prognoses of patients in some cancers. However, the immunological and prognostic roles of CRs in lung adenocarcinoma (LUAD) are still unclear. Here, we systematically revealed the correlations of CRs with immunological features and the survival in LUAD patients based on a cohort of gene expression datasets from the public TCGA and GEO databases and real RNA-seq data by an integrative analysis using a comprehensive bioinformatics method. Totals of 160 differentially expressed CRs (DECRs) were identified between LUAD and normal lung tissues, and two molecular prognostic subtypes (MPSs) were constructed and evaluated based on 27 prognostic DECRs using five independent datasets (p =0.016, <0.0001, =0.008, =0.00038 and =0.00055, respectively). Six differentially expressed genes (DEGs) (CENPK, ANGPTL4, CCL20, CPS1, GJB3, TPSB2) between two MPSs had the most important prognostic feature and a six-gene prognostic model was established. LUAD patients in the low-risk subgroup showed a higher overall survival (OS) rate than those in the high-risk subgroup in nine independent datasets (p <0.0001, =0.021, =0.016, =0.0099, <0.0001, =0.0045, <0.0001, =0.0038 and =0.00013, respectively). Six-gene prognostic signature had the highest concordance index of 0.673 compared with 19 reported prognostic signatures. The risk score was significantly correlated with immunological features and activities of oncogenic signaling pathways. LUAD patients in the low-risk subgroup benefited more from immunotherapy and were less sensitive to conventional chemotherapy agents. This study provides novel insights into the prognostic and immunological roles of CRs in LUAD.

Constructing an APOBEC-related gene signature with predictive value in the overall survival and therapeutic sensitivity in lung adenocarcinoma

Background

APOBEC family play an important role in cancer mutagenesis and tumor development. The role of APOBEC family in lung adenocarcinoma (LUAD) has not been studied comprehensively.

Materials and methods

The expression data of pan-cancer as well as LUAD was obtained from public databases. The expression level of APOBEC family genes was analyzed in different normal and cancer tissues. APOBEC mutagenesis enrichment score (AMES) was utilized to evaluate the APOBEC-induced mutations and the relation of APOBEC with genomic instability. Gene set enrichment analysis was used to identify differentially enriched pathways. Univariate Cox regression and Lasso regression were applied to screen key prognostic genes. The immune cell infiltration was estimated by CIBERSORT. RT-qPCR assay, CCK-8 and Transwell assay were conducted to explore gene expression and lung cancer cell invasion.

Results

Cancer tissues had significantly altered expression of APOBEC family genes and the expression patterns of APOBEC family were different in different cancer types. APOBEC3B was the most aberrantly expressed in most cancer types. In LUAD, we observed a significantly positive correlation of AMES with intratumor heterogeneity (ITH), tumor neoantigen burden (TNB), and tumor mutation burden (TMB). High AMES group had high mutation counts of DNA damage repair pathways, and high enrichment of cell cycle and DNA repair pathways. We identified four prognostic genes (LYPD3, ANLN, MUC5B, and FOSL1) based on AMES, and constructed an AMES-related gene signature. The expressions of four genes were enhanced and accelerated the invasion ability and viability of lung cancer cells. Furthermore, we found that high group increased oxidative stress level.

Conclusions

APOBEC family was associated with genomic instability, DNA damage-related pathways, and cell cycle in LUAD. The AMES-related gene signature had a great potential to indicate the prognosis and guide immunotherapy/chemotherapy for patients suffering from LUAD.

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis. Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients' drug sensitivity was predicted by the pRRophetic package in R language. Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study. Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.

Integrated analysis of NET-DNA receptor CCDC25 in malignant tumors: Pan-cancer analysis

Background

Previously, it was reported that the coiled-coil domain containing 25 (CCDC25) plays a role in the formation of neutrophil extracellular traps (NETs). This study systematically analyzed the expression profiles of CCDC25 in 30 different types of cancer and one type of blood cancer, acute myeloid leukemia.

Methods

The GTEx and CCLE databases were used to evaluate the distribution of CCDC25 expression in both normal tissue and cancer cell lines. A comparison was performed between normal tissue and tumor tissue to analyze the differential expression of CCDC25. We assessed the impact of CCDC25 on the clinical outlook in the TCGA pan-cancer data set by analyzing the Kaplan-Meier survival plot and conducting COX regression analysis. Moreover, the association between the expression levels of CCDC25 and the tumor microenvironment in multiple cancers was conducted. Additionally, the investigation also examined the link between CCDC25 and immune neoantigen, tumor mutational burden, microsatellite instability, mismatch repair genes (MMRs), HLA-related genes, and DNA methyltransferase (DNMT).

Results

CCDC25 was expressed in nearly all of the 31 normal tissues while exhibiting a moderate to low level of expression in cancer cell lines. While abnormal expression was detected in the majority of malignancies, there was no link found between elevated CCDC25 levels and overall survival, disease-free survival, recurrence-free survival, and disease-free interval in the TCGA comprehensive cancer data set. Nevertheless, the expression of CCDC25 exhibited a notable link with the infiltration levels of activated CD4 memory T cells, quiescent mast cells, dendritic cells in an activated state, T cells that assist in follicle development, M2 macrophages, and neutrophils in various tumors.

Conclusions

In most cancers, the results indicate that there is no link between CCDC25 and prognosis. However, CCDC25 can be targeted for therapeutic purposes concerning metastasis and immune infiltration.

Clinical implications and immune features of CENPN in breast cancer

BACKGROUND

A number of human diseases have been associated with Centromere protein N (CENPN), but its role in breast cancer is unclear.

METHODS

A pan-cancer database of Genotype Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA) were used to examine the expression of CENPN. Using TCGA clinical survival data and breast cancer specimens from our center for validation, the relationship between CENPN expression, breast cancer prognosis, and clinicopathological characteristics of patients was examined. Bioinformatics was utilized to conduct an enrichment study of CENPN. Additionally, the potential of CENPN as a predictive biomarker for immunotherapy success was confirmed by analyzing the co-expression of CENPN with immune-checkpoint related genes, reviewing the TCGA database, and evaluating the correlation between CENPN expression and immune cell infiltration. Using the CCK8 test and colony formation assay, CENPN was evaluated for its ability to inhibit breast cancer cell proliferation. Transwell assays and scratch tests were used to assess the impact of CENPN on breast cancer cell migration.

RESULTS

CENPN is found in a wide range of tumors, including breast cancer. Additional investigation revealed that CENPN was co-expressed with the majority of immune checkpoint-related genes, had the potential to serve as a predictive biomarker for immunotherapy effectiveness, and that high CENPN expression was linked to high Tregs and low CD8 + T cells and NK cells. Breast cancer cells' malignant characteristics, such as migration and cell proliferation, were inhibited by CENPN knockdown.

CONCLUSIONS

According to our findings, CENPN may be an oncogene in breast cancer, as well as a new therapeutic target for immune checkpoint inhibitors.

An integrative analysis reveals the prognostic value and potential functions of PSMD11 in hepatocellular carcinoma

The global burden of hepatocellular carcinoma (HCC) as a preeminent etiology of cancer-related mortalities sheds light on the imperative necessity for a more profound comprehension of its fundamental biological mechanisms. In this context, the precise function of the 26S proteasome non-ATPase regulatory subunit 11 (PSMD11) in HCC remains equivocal. To address this vital knowledge gap, we interrogated the cancer genome atlas, genotype-tissue expression, International cancer genome consortium, gene expression omnibus, the cancer cell line encyclopedia, and tumor immune single-cell hub databases to evaluate the expression pattern of PSMD11, further confirmed by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) in LO2, MHCC-97H, HepG2, and SMMC7721 cell lines. Additionally, we meticulously assessed the clinical significance and prognostic value of PSMD11, while also exploring its potential molecular mechanisms in HCC. Our findings demonstrated that PSMD11 was highly expressed in HCC tissues, correlating with pathologic stage and histologic grade, thereby conferring a poor prognosis. Mechanistically, PSMD11 appears to exert its tumorigenic effects through the modulation of tumor metabolism-related pathways. Impressively, low PSMD11 expression was associated with increased immune effector cell infiltration, heightened responsiveness to molecular targeted drugs such as dasatinib, erlotinib, gefitinib, and imatinib, as well as reduced somatic mutation rate. Additionally, we demonstrated that PSMD11 might modulate HCC development through intricate interactions with cuproptosis-related genes ATP7A, DLAT, and PDHA1. Our comprehensive analyses collectively suggest that PSMD11 represents a promising therapeutic target in HCC.

Expression of PD-L1 and VISTA in Intraductal Papillary Mucinous Neoplasm With Associated Invasive Carcinoma of the Pancreas

Early detection and treatment of invasive carcinoma arising in association with intraductal papillary mucinous neoplasm (IPMN), which is biologically and (epi)genetically distinct from conventional pancreatic ductal adenocarcinoma, provide an opportunity to improve the prognosis of this lethal disease. Despite the successful application of programmed death (ligand) 1 (PD-[L]1)-blocking strategies in numerous cancers, the immune microenvironment of IPMN with associated invasive carcinoma remains elusive. Here, we investigated CD8+ T cells, CD68+ macrophages, PD-L1, and V-domain immunoglobulin suppressor of T-cell activation (VISTA) in 60 patients with IPMN with associated invasive carcinoma using immunohistochemistry, explored their correlations with clinicopathologic variables and prognosis, and compared them with those in 76 patients with IPMN without invasive carcinoma (60 low-grade and 16 high-grade lesions). Using antibodies against CD8, CD68, and VISTA, we evaluated tumor-infiltrating immune cells in 5 high-power fields (×400) and calculated the corresponding mean counts. PD-L1 with a combined positive score of ≥1 was regarded as positive, and VISTA expression on tumor cells (TCs) was deemed positive when ≥1% of TCs showed membranous/cytoplasmic staining. A reduction of CD8+ T cells and an increase of macrophages were observed during carcinogenesis. Positive PD-L1 combined positive score and VISTA expression on TCs were 13% and 11% in the intraductal component of IPMN with associated invasive carcinoma, 15% and 12% in the associated invasive carcinoma, and 6% and 4% in IPMN without an invasive carcinoma, respectively. Interestingly, the PD-L1 positivity rate was the highest in a subset of associated invasive carcinomas (predominantly gastric-type-derived) and was associated with higher counts of CD8+ T cells, macrophages, and VISTA+ immune cells. Accumulation of VISTA+ immune cells was observed in the intraductal component of IPMN with associated invasive carcinoma compared with that of low-grade IPMN, whereas in intestinal-type IPMN with associated invasive carcinoma, the number of these cells decreased during the transition from the intraductal component to the associated invasive carcinoma. Survival analysis revealed that a higher number of macrophages predicted poorer prognosis. In conclusion, our results might help in individualized immunotherapeutic strategies for these patients.

Establishing Molecular Subgroups of CD8+ T Cell-Associated Genes in the Ovarian Cancer Tumour Microenvironment and Predicting the Immunotherapy Response

BACKGROUND

The mechanism by which infiltrating CD8+ T lymphocytes in the tumour microenvironment influence the survival of patients with ovarian cancer (OC) remains unclear.

METHODS

To identify biomarkers to optimise OC treatment, 13 immune-cell-line-associated datasets, RNA sequencing data, and clinical data from the GEO, TCGA, and the ICGC were collected. Gene expression in OC was assessed using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) staining.

RESULTS

We identified 520 genes and three immunological clusters (IC1, IC2, and IC3) associated with CD8+ T cells. Higher IFN scores, immune T cell lytic activity, and immune cell infiltration and upregulated expression of immune-checkpoint-related genes indicated that IC3 is more responsive to immunotherapy, whereas IC1 and IC2 have a poorer prognosis. A 10-gene signature, including SEMA4F, CX3CR1, STX7, PASK, AKIRIN2, HEMGN, GBP5, NSG1, and CXorf65, was constructed, and a multivariate Cox regression analysis revealed a significant association between the 10-gene signature-based risk model and overall survival (p < 0.001). A nomogram was constructed with age and the 10-gene signature. Consistent with the bioinformatics analysis, IHC and qRT-PCR confirmed the accuracy of the signatures in OC tissue samples. The predictive ability of the risk model was demonstrated using the Imvigor210 immunotherapy dataset.

CONCLUSIONS

The development of a novel gene signature associated with CD8+ T cells could facilitate more accurate prognostics and prediction of the immunotherapeutic response of patients with OC.

Comprehensive Analysis of SLC35A2 in Pan-Cancer and Validation of Its Role in Breast Cancer

Purpose

Elucidation of the oncogenic role of SLC35A2 in human tumors and the potential function and clinical significance in breast cancer.

Methods

Pan-cancer analysis was performed via various bioinformatics tools to explain the pathogenic role of SLC35A2. A prognostic nomogram was also developed based on the SLC35A2 expression and clinicopathological characteristics in breast cancer patients. In addition, the role of SLC35A2 was validated in breast cancer by in vivo and in vitro experiments.

Results

SLC35A2 expression is increased in 27 tumor types, and its high expression is substantially correlated with poor prognosis in patients with a variety of cancers. Receiver operating characteristic (ROC) curves showed that SLC35A2 expression levels could accurately distinguish most tumor tissues from normal tissues. High SLC35A2 expression was linked to increased immune infiltration in myeloid-derived suppressor cells (MDSC), as well as immune checkpoints, ferroptosis-related genes, tumor mutational burden (TMB), and microsatellite instability (MSI). SLC35A2 may be involved in tumorigenesis by regulating the glycosylation process. Furthermore, multivariate Cox analysis showed that SLC35A2 was an independent prognostic factor for breast cancer. And the nomogram model had good predictive accuracy for the prognosis of breast cancer patients. Meanwhile, cellular experiments demonstrated that knockdown of SLC35A2 could significantly inhibit the proliferation, migration and invasion of breast cancer cells, while increasing the protein level of E-cadherin and decreasing N-cadherin. A nude mouse xenograft model showed that inhibition of SLA35A2 expression could significantly inhibit tumor growth.

Conclusion

SLC35A2 has good diagnostic and prognostic values in multiple cancers and is closely related to tumor immune infiltration. In addition, SLA35A2 as an oncogene in breast cancer may be involved in the progression of epithelial mesenchymal transition (EMT).

Construction of subtype classifiers and validation of a prognostic risk model based on hypoxia-associated lncRNAs for lung adenocarcinoma

BACKGROUND

Studies have shown that long non-coding RNAs (lncRNAs) are found to be hypoxia-regulated lncRNAs in cancer. Lung adenocarcinoma (LUAD) is the leading cause of cancer death worldwide, and despite early surgical removal, has a poor prognosis and a high recurrence rate. Thus, we aimed to identify subtype classifiers and construct a prognostic risk model using hypoxia-associated long noncoding RNAs (hypolncRNAs) for LUAD.

METHODS

Clinical data of LUAD samples with prognosis information obtained from the Gene Expression Omnibus (GEO), acted as validation dataset, and The Cancer Genome Atlas (TCGA) databases, served as training dataset, were used to screen hypolncRNAs in each dataset by univariate Cox regression analysis; the intersection set was used for subsequent analyses. Unsupervised clustering analysis was performed based on the expression of hypolncRNAs using the 'ConsensuClusterPlus' package. The tumor microenvironment (TME) was compared between LUAD subgroups by analyzing the expression of immune cell infiltration, immune components, stromal components, immune checkpoints, and chemokine secretion. To identify robust prognostically associated hypolncRNAs and construct a risk score model, multivariate Cox regression analysis was performed.

RESULTS

A total of 14 hypolncRNAs were identified. Based on the expression of these hypolncRNAs, patients with LUAD were classified into three hypolncRNA-regulated subtypes. The three subtypes differed significantly in immune cell infiltration, stromal score, specific immune checkpoints, and secretion of chemokines and their receptors. The Tumor Immune Dysfunction and Exclusion (TIDE) scores and myeloid-derived suppressor cell (MDSC) scores were also found to differ significantly among the three hypolncRNA-regulated subtypes. Four of the 14 hypolncRNAs were used to construct a signature to distinguish the overall survival (OS) in TCGA dataset (P<0.0001) and GEO dataset (P=0.0032) and sensitivity to targeted drugs in patients at different risks of LUAD.

CONCLUSIONS

We characterized three regulatory subtypes of hypolncRNAs with different TMEs. We developed a signature based on hypolncRNAs, contributing to the development of personalized therapy and representing a new potential therapeutic target for LUAD.

The linkage of NF-κB signaling pathway-associated long non-coding RNAs with tumor microenvironment and prognosis in cervical cancer

BACKGROUND

NF-κB signaling pathway participate closely in regulating inflammation and immune response in many cancers. Long non-coding RNAs (lncRNAs) associated with NF-κB signaling have not been characterized in cervical cancer. This study revealed the linkage between tumor microenvironment and NF-κB signaling-associated lncRNAs in cervical cancer.

MATERIALS AND METHODS

The expression profiles of cervical cancer samples from The Cancer Genome Atlas (TCGA) database were downloaded. NF-κB signaling-associated lncRNAs were screened as a basis to perform molecular subtyping. Immune cell infiltration was assessed by ESTIMATE, Microenvironment Cell Populations (MCP)-counter and single sample gene set enrichment analysis (ssGSEA). The key NF-κB signaling-associated lncRNAs were identified by univariate analysis, least absolute shrinkage and selection operator, and stepAIC.

RESULTS

Three molecular subtypes or clusters (cluster 3, cluster 2, and cluster 1) were categorized based on 27 prognostic NF-κB signaling-associated lncRNAs. Cluster 2 had the worst prognosis, highest immune infiltration, as well as the highest expression of most of immune checkpoints. Three clusters showed different sensitivities to immunotherapy and chemotherapy. Six key NF-κB signaling-associated lncRNAs were screened to establish a six-lncRNA risk model for predicting cervical cancer prognosis.

CONCLUSIONS

NF-κB signaling-associated lncRNAs played an important role in regulating immune microenvironment. The subtyping based on NF-κB signaling-associated lncRNAs may assist in the selection of optimal treatments. The six key NF-κB signaling-associated lncRNAs could act as prognostic biomarkers in prognostic prediction for cervical cancer.

Development of a circHIPK3-based ceRNA network and identification of mRNA signature in breast cancer patients harboring BRCA mutation

Background

Exploring the regulatory network of competing endogenous RNAs (ceRNAs) as hallmarks for breast cancer development has great significance and could provide therapeutic targets. An mRNA signature predictive of prognosis and therapy response in BRCA carriers was developed according to circular RNA homeodomain-interacting protein kinase 3 (circHIPK3)-based ceRNA network.

Method

We constructed a circHIPK3-based ceRNA network based on GSE173766 dataset and identified potential mRNAs that were associated with BRCA mutation patients within this ceRNA network. A total of 11 prognostic mRNAs and a risk model were identified and developed by univariate Cox regression analysis and the LASSO regression analysis as well as stepAIC method. Genomic landscape was treated by mutect2 and fisher. Immune characteristics was analyzed by ESTIMATE, MCP-counter. TIDE analysis was conducted to predict immunotherapy. The clinical treatment outcomes of BRCA mutation patients were assessed using a nomogram. The proliferation, migration and invasion in breast cancer cell lines were examined using CCK8 assay and transwell assay.

Result

We found 241 mRNAs within the circHIPK3-based ceRNA network. An 11 mRNA-based signature was identified for prognostic model construction. High risk patients exhibited dismal prognosis, low response to immunotherapy, less immune cell infiltration and tumor mutation burden (TMB). High-risk patients were sensitive to six anti-tumor drugs, while low-risk patient were sensitive to 47 drugs. The risk score was the most effective on evaluating patients' survival. The robustness and good prediction performance were validated in The Cancer Genome Atlas (TCGA) dataset and immunotherapy datasets, respectively. In addition, circHIPK3 mRNA level was upregulated, and promoted cell viability, migration and invasion in breast cancer cell lines.

Conclusion

The current study could improve the understanding of mRNAs in relation to BRCA mutation and pave the way to develop mRNA-based therapeutic targets for breast cancer patients with BRCA mutation.

Development and validation of a ligand-receptor pairs signature to predict outcome and provide a therapeutic strategy in gastric cancer

BACKGROUND

An important factor in tumor development and progression is the tumor microenvironment (TME), which is heterogeneous. Previous studies have mainly investigated the expression profile and prognostic values of genes in gastric cancer (GC) at the cell population level but neglected the interactions and heterogeneity between cells.

METHODS

The pattern of ligand-receptor (LR) interactions was delineated on a scRNA-seq dataset containing 44,953 cells from nine GC patients and a fourth bulk RNA-seq dataset including data from 1159 GC patients. We then constructed an LR.Score scoring model to comprehensively evaluate the influence of LR-pairs on the TME, overall survival, and immunotherapy response in GC patients from several cohorts.

RESULTS

Cell communication network among 13 cell types was constructed based on the LR-pairs. We proposed a new molecular subtyping model for GC based on the LR-pairs and revealed the differences in prognosis, pathophysiologic features, mutation characteristics, function enrichment, and immunological characteristics among the three subtypes. Finally, an LR.Score model based on LR-pairs was developed and validated on several datasets.

CONCLUSIONS

Based on the selected LR-pairs, we successfully constructed a novel prediction model and observed its well performance on molecular subtyping, target and pathway screening, prognosis judging, and immunotherapy response predicting.

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

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

A novel prognostic model for hepatocellular carcinoma based on pyruvate metabolism-related genes

Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer, accounting for over 90% of cases. As pyruvate metabolic pathways are often dysregulated in cancer cells, investigating pyruvate metabolism-related genes may help identify prognostic gene signature and develop potential strategies for the management of patients with HCC. The mRNA expression profile, gene mutation data, and clinical information of HCC were obtained from open-source databases. A list of pyruvate metabolism-related genes was downloaded from the MSigDB dataset. Our findings revealed that certain pyruvate metabolism-related genes had copy number variations and single nucleotide variations in patients with liver cancer. Based on pyruvate metabolism-related genes, we stratified patients with HCC into three subtypes with different prognoses, clinical features, mutation profiles, functional annotation, and immune infiltration status. Next, we identified 13 key pyruvate metabolism-related genes significantly correlated with the prognosis of HCC using six machine learning algorithms and constructed a risk model. We also observed that the risk score was positively associated with a worse prognosis and increased immune infiltration. In summary, our study established a prognostic risk model for HCC based on pyruvate metabolism-related genes, which may contribute to the identification of potential prognostic targets and the development of new clinical management strategies for HCC.

Constructing a cancer stem cell related prognostic model for predicting immune landscape and drug sensitivity in colorectal cancer

Background: Colorectal cancer (CRC) ranks the second malignancy with high incidence and mortality worldwide. Cancer stem cells (CSCs) function critically in cancer progression and metastasis via the interplay with immune cells in tumor microenvironment. This study aimed to identify important CSC marker genes and parsed the role of these marker genes in CRC. Materials and methods: CRC samples' single-cell RNA sequencing data and bulk transcriptome data were utilized. Seurat R package annotated CSCs and identified CSC marker genes. Consensus clustering subtyped CRC samples based on CSC marker genes. Immune microenvironment, pathway and oxidative stress analysis was performed using ESTIMATE, MCP-counter analysis and ssGSEA analysis. A prognostic model was established by Lasso and stepAIC. Sensitivity to chemotherapeutic drugs was determined by the biochemical half maximal inhibitory concentration with pRRophetic R package. Results: We identified a total of 29 CSC marker genes related to disease-specific survival (DSS). Two clusters (CSC1 and CSC2) were determined, and CSC2 showed shorter DSS, a larger proportion of late-stage samples, and higher oxidative stress response. Two clusters exhibited differential activation of biological pathways associated with immune response and oncogenic signaling. Drug sensitivity analysis showed that 44 chemotherapy drugs were more sensitive to CSC2 that those in CSC1. We constructed a seven-gene prognostic model (DRD4, DPP7, UCN, INHBA, SFTA2, SYNPO2, and NXPH4) that was effectively to distinguish high-risk and low-risk patients. 14 chemotherapy drugs were more sensitive to high-risk group and 13 chemotherapy drugs were more sensitive to low-risk group. Combination of higher oxidative stress and risk score indicated dismal prognosis. Conclusion: The CSC marker genes we identified may help to further decipher the role of CSCs in CRC development and progression. The seven-gene prognostic model could serve as an indicator for predicting the response to immunotherapy and chemotherapy as well as prognosis of CRC patients.

Based on cuproptosis-related lncRNAs, a novel prognostic signature for colon adenocarcinoma prognosis, immunotherapy, and chemotherapy response

Introduction: Colon adenocarcinoma (COAD) is a special pathological subtype of colorectal cancer (CRC) with highly heterogeneous solid tumors with poor prognosis, and novel biomarkers are urgently required to guide its prognosis. Material and methods: RNA-Seq data of COAD were downloaded through The Cancer Genome Atlas (TCGA) database to determine cuproptosis-related lncRNAs (CRLs) using weighted gene co-expression network analysis (WGCNA). The scores of the pathways were calculated by single-sample gene set enrichment analysis (ssGSEA). CRLs that affected prognoses were determined via the univariate COX regression analysis to develop a prognostic model using multivariate COX regression analysis and LASSO regression analysis. The model was assessed by applying Kaplan-Meier (K-M) survival analysis and receiver operating characteristic curves and validated in GSE39582 and GSE17538. The tumor microenvironment (TME), single nucleotide variants (SNV), and immunotherapy response/chemotherapy sensitivity were assessed in high- and low-score subgroups. Finally, the construction of a nomogram was adopted to predict survival rates of COAD patients during years 1, 3, and 5. Results: We found that a high cuproptosis score reduced the survival rates of COAD significantly. A total of five CRLs affecting prognosis were identified, containing AC008494.3, EIF3J-DT, AC016027.1, AL731533.2, and ZEB1-AS1. The ROC curve showed that RiskScore could perform well in predicting the prognosis of COAD. Meanwhile, we found that RiskScore showed good ability in assessing immunotherapy and chemotherapy sensitivity. Finally, the nomogram and decision curves showed that RiskScore would be a powerful predictor for COAD. Conclusion: A novel prognostic model was constructed using CRLs in COAD, and the CRLs in the model were probably a potential therapeutic target. Based on this study, RiskScore was an independent predictor factor, immunotherapy response, and chemotherapy sensitivity for COAD, providing a new scientific basis for COAD prognosis management.

Distribution characteristics and clinical significance of infiltrating T cells in the tumor microenvironment of pancreatic cancer

Tumor-infiltrating lymphocytes (TILs) are important components of the tumor microenvironment (TME). However, the distribution characteristics of TILs and their significance in pancreatic cancer (PC) remain largely unexplored. The levels of TILs, including the total number of T cells, cluster of differentiation (CD)4⁺ T cells, CD8⁺ cytotoxic T lymphocytes (CTLs), regulatory T-cells (Tregs), programmed cell death protein 1⁺ T cells and programmed cell death ligand 1 (PD-L1)⁺ T cells, in the TME of patients with PC were detected using multiple fluorescence immunohistochemistry. The associations between the number of TILs and the clinicopathological characteristics were investigated using χ² tests. In addition, Kaplan-Meier survival and Cox regression analyses were used to assess the prognostic value of these TIL types. Compared with paracancerous tissues, in PC tissues, the proportions of total T cells, CD4⁺ T cells and CD8⁺ CTLs were markedly decreased, while those of Tregs and PD-L1⁺ T cells were significantly increased. The levels of CD4⁺ T cell and CD8⁺ CTL infiltrates were inversely associated with tumor differentiation. Higher infiltrates of Tregs and PD-L1⁺ T cells were closely associated with advanced N and TNM stages. It is important to note that the infiltrates of total T cells, CD4⁺ T cells, Tregs and PD-L1⁺ T cells in the TME were independent risk factors for the prognosis of PC. PC was characterized by an immunosuppressive TME with a decrease in the number of CD4⁺ T cells and CD8⁺ CTLs, and an increase in the number of Tregs and PD-L1⁺ T cells. Overall, the number of total T cells, CD4⁺ T cells, Tregs and PD-L1⁺ T cells in the TME was a potential predictive marker for the prognosis of PC.

Identification and validation of TME-related signatures to predict prognosis and response to anti-tumor therapies in skin cutaneous melanoma

The tumor microenvironment (TME) dynamically regulates cancer progression and affects clinical outcomes. This study aimed to identify molecular subtypes and construct a prognostic risk model based on TME-related signatures in skin cutaneous melanoma (SKCM) patients. We categorized SKCM patients based on transcriptome data of SKCM from The Cancer Genome Atlas (TCGA) database and 29 TME-related gene signatures. Differentially expressed genes were identified using univariate Cox regression and Lasso regression analysis, which were used for risk model construction. The robustness of this model was validated in independent external cohorts. Genetic landscape alterations, immune characteristics, and responsiveness to immunotherapy/chemotherapy were evaluated. Three TME-related subtypes were identified, and subtype C3 exhibited the most favorable prognosis, had enriched immune-related pathways, and possessed more infiltration of T_cells_CD8, T_cells_CD4_memory_activated, and Macrophages_M1 but a lower TumorPurity, whereas Macrophages_M2 were increased in subtype C1 and subtype C2. Subtype C1 was more sensitive to Cisplatin, subtype C2 was more sensitive to Temozolomide, and subtype C3 was more sensitive to Paclitaxel; 8 TME-related genes (NOTCH3, HEYL, ZNF703, ABCC2, PAEP, CCL8, HAPLN3, and HPDL) were screened for risk model construction. High-risk patients had dismal prognosis with good prediction performance. Moreover, low-risk patients were more sensitive to Paclitaxel and Temozolomide, whereas high-risk patients were more sensitive to Cisplatin. This risk model had robustness in predicting prognosis in SKCM patients. The results facilitate the understanding of TME-related genes in SKCM and provide a TME-related genes-based predictive model in prognosis and direction of personalized options for SKCM patients.

Targeting MFAP5 in cancer-associated fibroblasts sensitizes pancreatic cancer to PD-L1-based immunochemotherapy via remodeling the matrix

Highly desmoplastic and immunosuppressive tumor microenvironment (TME) in pancreatic ductal adenocarcinoma (PDAC) contributes to tumor progression and resistance to current therapies. Clues targeting the notorious stromal environment have offered hope for improving therapeutic response whereas the underlying mechanism remains unclear. Here, we find that prognostic microfibril associated protein 5 (MFAP5) is involved in activation of cancer-associated fibroblasts (CAFs). Inhibition of MFAP5^highCAFs shows synergistic effect with gemcitabine-based chemotherapy and PD-L1-based immunotherapy. Mechanistically, MFAP5 deficiency in CAFs downregulates HAS2 and CXCL10 via MFAP5/RCN2/ERK/STAT1 axis, leading to angiogenesis, hyaluronic acid (HA) and collagens deposition reduction, cytotoxic T cells infiltration, and tumor cells apoptosis. Additionally, in vivo blockade of CXCL10 with AMG487 could partially reverse the pro-tumor effect from MFAP5 overexpression in CAFs and synergize with anti-PD-L1 antibody to enhance the immunotherapeutic effect. Therefore, targeting MFAP5^highCAFs might be a potential adjuvant therapy to enhance the immunochemotherapy effect in PDAC via remodeling the desmoplastic and immunosuppressive microenvironment.

Function and prognostic value of basement membrane -related genes in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) has become a common cause of cancer-related death. Many studies have shown that the basement membrane (BM) is associated with the development of cancer. However, BM-related gene expression and its relationship to LUAD prognosis remains unclear. Methods: BM-related genes from previous studies were used. Clinical and mRNA expression information were obtained from TCGA database. Cox, minimum absolute contraction, and selection operator regression were applied to analyze the selected genes affecting LUAD prognosis. A prognostic-risk model was then established. Furthermore, this study applied Kaplan-Meier analysis to assess the outcomes of high- and low-risk groups, then explored their differences in drug sensitivity. The DSigDB database was used to screen for therapeutic small-molecule drugs. Results: Fourteen prognostic models based on BM-related genes were successfully constructed and validated in patients with LUAD. We also found that independence was a prognostic factor in all 14 BM-based models. Functional analysis showed that the enrichment of BM-related genes mainly originated from signaling pathways related to cancer. The BM-based model also suggested that immune cell infiltration is associated with checkpoints. The low-risk patients may benefit from cyclopamine and docetaxel treatments. Conclusion: This study identified a reliable biomarker to predict survival in patients with LUAD and offered new insights into the function of BM-related genes in LUAD.

Identification of basement membrane-related prognostic signature for predicting prognosis, immune response and potential drug prediction in papillary renal cell carcinoma

Papillary renal cell carcinoma (PRCC) is a malignant neoplasm of the kidney and is highly interesting due to its increasing incidence. Many studies have shown that the basement membrane (BM) plays an important role in the development of cancer, and structural and functional changes in the BM can be observed in most renal lesions. However, the role of BM in the malignant progression of PRCC and its impact on prognosis has not been fully studied. Therefore, this study aimed to explore the functional and prognostic value of basement membrane-associated genes (BMs) in PRCC patients. We identified differentially expressed BMs between PRCC tumor samples and normal tissue and systematically explored the relevance of BMs to immune infiltration. Moreover, we constructed a risk signature based on these differentially expressed genes (DEGs) using Lasso regression analysis and demonstrated their independence using Cox regression analysis. Finally, we predicted 9 small molecule drugs with the potential to treat PRCC and compared the differences in sensitivity to commonly used chemotherapeutic agents between high and low-risk groups to better target patients for more precise treatment planning. Taken together, our study suggested that BMs might play a crucial role in the development of PRCC, and these results might provide new insights into the treatment of PRCC.

Tissue-Resident Memory T Cells in Pancreatic Ductal Adenocarcinoma Coexpress PD-1 and TIGIT and Functional Inhibition Is Reversible by Dual Antibody Blockade

Pancreatic ductal adenocarcinoma (PDAC) has a poor clinical outlook. Responses to immune checkpoint blockade are suboptimal and a much more detailed understanding of the tumor immune microenvironment is needed if this situation is to be improved. Here, we characterized tumor-infiltrating T-cell populations in patients with PDAC using cytometry by time of flight (CyTOF) and single-cell RNA sequencing. T cells were the predominant immune cell subset observed within tumors. Over 30% of CD4+ T cells expressed a CCR6+CD161+ Th17 phenotype and 17% displayed an activated regulatory T-cell profile. Large populations of CD8+ tissue-resident memory (TRM) T cells were also present and expressed high levels of programmed cell death protein 1 (PD-1) and TIGIT. A population of putative tumor-reactive CD103+CD39+ T cells was also observed within the CD8+ tumor-infiltrating lymphocytes population. The expression of PD-1 ligands was limited largely to hemopoietic cells whilst TIGIT ligands were expressed widely within the tumor microenvironment. Programmed death-ligand 1 and CD155 were expressed within the T-cell area of ectopic lymphoid structures and colocalized with PD-1+TIGIT+ CD8+ T cells. Combinatorial anti-PD-1 and TIGIT blockade enhanced IFNγ secretion and proliferation of T cells in the presence of PD-1 and TIGIT ligands. As such, we showed that the PDAC microenvironment is characterized by the presence of substantial populations of TRM cells with an exhausted PD-1+TIGIT+ phenotype where dual checkpoint receptor blockade represents a promising avenue for future immunotherapy.

Systematic analysis of lncRNA gene characteristics based on PD-1 immune related pathway for the prediction of non-small cell lung cancer prognosis

BACKGROUND

Non-small cell lung cancer (NSCLC) is heterogeneous. Molecular subtyping based on the gene expression profiles is an effective technique for diagnosing and determining the prognosis of NSCLC patients.

METHODS

Here, we downloaded the NSCLC expression profiles from The Cancer Genome Atlas and the Gene Expression Omnibus databases. ConsensusClusterPlus was used to derive the molecular subtypes based on long-chain noncoding RNA (lncRNA) associated with the PD-1-related pathway. The LIMMA package and least absolute shrinkage and selection operator (LASSO)-Cox analysis were used to construct the prognostic risk model. The nomogram was constructed to predict the clinical outcomes, followed by decision curve analysis (DCA) to validate the reliability of this nomogram.

RESULTS

We discovered that PD-1 was strongly and positively linked to the T-cell receptor signaling pathway. Furthermore, we identified two NSCLC molecular subtypes yielding a significantly distinctive prognosis. Subsequently, we developed and validated the 13-lncRNA-based prognostic risk model in the four datasets with high AUC values. Patients with low-risk showed a better survival rate and were more sensitive to PD-1 treatment. Nomogram construction combined with DCA revealed that the risk score model could accurately predict the prognosis of NSCLC patients.

CONCLUSIONS

This study demonstrated that lncRNAs engaged in the T-cell receptor signaling pathway played a significant role in the onset and development of NSCLC, and that they could influence the sensitivity to PD-1 treatment. In addition, the 13 lncRNA model was effective in assisting clinical treatment decision-making and prognosis evaluation.

Single-Cell Sequencing-Based Validation of T Cell-Associated Diagnostic Model Genes and Drug Response in Crohn's Disease

Crohn's disease is a highly heterogeneous autoimmune disease with a unique inflammatory phenotype of T cells at the lesion site. We aim to further explore the diagnosis of Crohn's disease and drug prediction of T cell marker gene expression. We obtained single-cell expression profile data from 22 CDs or normal samples and performed cell annotation and cellular communication analysis. Through the intersection of T cell marker genes, differential genes, and WGCNA results, we identified T cell-specific key genes and their immune landscapes and potential pathogenesis, and validated them across multiple datasets and patient tissue samples. We also explored the differentiation characteristics of genes by pseudo-temporal analysis and assessed their diagnostic performance and drug sensitivity by molecular docking. Finally, we extended this study to the prognosis of IBD-associated colon cancer. TNF-centered 5-gene diagnostic model not only has excellent diagnostic efficacy, but is also closely associated with KRAS, P53, and IL6/JAK/STAT3 pathways and physiological processes, such as EMT, coagulation, and apoptosis. In addition, this diagnostic model may have potential synergistic immunotherapeutic effects, with positive correlations with immune checkpoints such as CTLA4, CD86, PDCD1LG2, and CD40. Molecular docking demonstrated that BIRC3 and ANXA1 have strong binding properties to Azathioprine and Glucoocorticoid. Furthermore, the 5-gene model may suggest antagonism to IFX and prognosis for colon cancer associated with inflammatory bowel disease. Single-cell sequencing targeting T cell-related features in patients with Crohn's disease may aid in new diagnostic decisions, as well as the initial exploration of high-potential therapies.

Establishing a prognostic model of chromatin modulators and identifying potential drug candidates in renal clear cell patients

BACKGROUND

Renal carcinoma is a common malignant tumor of the urinary system. Advanced renal carcinoma has a low 5-year survival rate and a poor prognosis. More and more studies have confirmed that chromatin regulators (CRs) can regulate the occurrence and development of cancer. This article investigates the functional and prognostic value of CRs in renal carcinoma patients.

METHODS

mRNA expression and clinical information were obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and LASSO regression analysis were used to select prognostic chromatin-regulated genes and use them to construct a risk model for predicting the prognosis of renal cancer. Differences in prognosis between high-risk and low-risk groups were compared using Kaplan-Meier analysis. In addition, we analyzed the relationship between chromatin regulators and tumor immune infiltration, and explored differences in drug sensitivity between risk groups.

RESULTS

We constructed a model consisting of 11 CRs to predict the prognosis of renal cancer patients. We not only successfully validated its feasibility, but also found that the 11 CR-based model was an independent prognostic factor. Functional analysis showed that CRs were mainly enriched in cancer development-related signalling pathways. We also found through the TIMER database that CR-based models were also associated with immune cell infiltration and immune checkpoints. At the same time, the genomics of drug sensitivity in cancer database was used to analyze the commonly used drugs of renal clear cell carcinoma patients. It was found that patients in the low-risk group were sensitive to medicines such as axitinib, pazopanib, sorafenib, and gemcitabine. In contrast, those in the high-risk group may be sensitive to sunitinib.

CONCLUSION

The chromatin regulator-related prognostic model we constructed can be used to assess the prognostic risk of patients with clear cell renal cell carcinoma. The results of this study can bring new ideas for targeted therapy of clear cell renal carcinoma, helping doctors to take corresponding measures in advance for patients with different risks.

Characterization of tumor microenvironment and programmed death-related genes to identify molecular subtypes and drug resistance in pancreatic cancer

Background: Immunotherapy has been a key option for the treatment of many types of cancer. A positive response to immunotherapy is heavily dependent on tumor microenvironment (TME) interaction. However, in pancreatic adenocarcinoma (PAAD), the association between TME mode of action and immune cell infiltration and immunotherapy, clinical outcome remained unknown.

Methods: We systematically evaluated 29 TME genes in PAAD signature. Molecular subtypes of distinct TME signatures in PAAD were characterized by consensus clustering. After this, we comprehensively analyzed their clinical features, prognosis, and immunotherapy/chemotherapy response using correlation analysis, Kaplan-Meier curves analysis, ssGSEA analysis. 12 programmed cell death (PCD) patterns were acquired from previous study. Differentially expressed genes (DEGs) were acquired based on differential analysis. Key genes affecting overall survival (OS) of PAAD were screened by COX regression analysis and used to develop a RiskScore evaluation model. Finally, we assessed the value of RiskScore in predicting prognosis and treatment response in PAAD.

Results: We identified 3 patterns of TME-associated molecular subtypes (C1, C2, C3), and observed that clinicopathological characteristics, prognosis, pathway features and immune features, immunotherapy/chemosensitivity of patients were correlated with the TME related subtypes. C1 subtype was more sensitive to the four chemotherapeutic drugs. PCD patterns were more likely to occur at C2 or C3. At the same time, we also detected 6 key genes that could affect the prognosis of PAAD, and 5 genes expressions were closely associated to methylation level. Low-risk patients with high immunocompetence had favorable prognostic results and high immunotherapy benefit. Patients in the high-risk group were more sensitive to chemotherapeutic drugs. RiskScore related to TME was an independent prognostic factor for PAAD.

Conclusion: Collectively, we identified a prognostic signature of TME in PAAD patients, which could help elucidate the specific mechanism of action of TME in tumors and help to explore more effective immunotherapy strategies.