The Comprehensive Analysis Identified an Autophagy Signature for the Prognosis and the Immunotherapy Efficiency Prediction in Lung Adenocarcinoma

Transcriptomic profiles of single-cell autophagy-related genes (ATGs) in lung diseases

Autophagy related genes (ATGs) play essential roles in maintaining cellular functions, although biological and pathological alterations of ATG phenotypes remain poorly understood. To address this knowledge gap, we utilized the single-cell sequencing technology to elucidate the transcriptomic atlas of ATGs in lung diseases, with a focus on lung epithelium and lymphocytes. This study conducted a comprehensive investigation into RNA profiles of ATGs in the lung tissues obtained from healthy subjects and patients with different lung diseases through single-cell RNA sequencing (scRNA-seq), including COVID-19 related acute lung damage, idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), systemic sclerosis (SSC), and lung adenocarcinoma (LUAD). Our findings revealed significant variations of ATGs expression across lung epithelial cell subsets, e.g., over-expression of MAPK8 in basal cells, ATG10 in club cells, and BCL2 in a goblet cell subset. The changes of autophagy-related pathways varied between lung epithelial and lymphocyte subsets. We identified the disease-associated changes in ATG expression, including significant alterations in BCL2, BCL2L1, PRKCD, and PRKCQ in inflammatory lung diseases (COPD and IPF), and MAP2K7, MAPK3, and RHEB in lung cancer (LUAD), as compared to normal lung tissues. Key ligand-receptor pairs (e.g., CD6-ALCAM, CD99-CD99) and signaling pathways (e.g., APP, CD74) might serve as biomarkers for lung diseases. To evaluate ATGs responses to external challenges, we examined ATGs expression in different epithelial cell lines exposed to cigarette smoking extract (CSE), lysophosphatidylcholine (lysoPC), lipopolysaccharide (LPS), and cholesterol at various doses and durations. Notable changes were observed in CFLAR, EIF2S1, PPP2CA, and PPP2CB in A549 and H1299 against CSE and LPS. The heterogeneity of ATGs expression was dependent on cell subsets, pathologic conditions, and challenges, as well as varied among cellular phenotypes, functions, and behaviors, and the severity of lung diseases. In conclusion, our data might provide new insights into the roles of ATGs in epithelial biology and pulmonary disease pathogenesis, with implications for disease progression and prognosis.

Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence

Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway with immunotherapies has shown promise as a novel glioma treatment. However, not all patients experience long-lasting benefits, underscoring the necessity to discover reliable biomarkers for predicting treatment outcomes. This study applied a range of advanced artificial intelligence methods to identify a new biomarker linked to the effectiveness of anti-PD-1 immunotherapy in glioma patients. Through an extensive analysis of single-cell RNA sequencing and bulk transcriptomic data from over 3000 patients, the gene SLFN12 emerged as a significant and independent predictor of immunotherapy response. Our results indicate that elevated SLFN12 expression is associated with worse overall survival across various glioma cohorts. Notably, we found that patients with high SLFN12 levels are less likely to respond favourably to anti-PD-1 treatment, positioning SLFN12 as a clinically valuable biomarker for personalised treatment decisions. Functional studies revealed that SLFN12 is involved in key immune-related pathways, shedding light on its potential role in altering the tumour microenvironment and impacting immunotherapy outcomes. Additional laboratory experiments confirmed the role of SLFN12 in promoting glioma cell proliferation, migration and macrophage recruitment. In summary, this study identifies SLFN12 as a novel biomarker for predicting immunotherapy response in glioma patients, offering new insights for precision immunotherapy approaches.

Development of a novel centrosome-related risk signature to predict prognosis and treatment response in lung adenocarcinoma

BACKGROUND

Abnormalities of centrosomes, the major microtubular organizing centers of animal cells and regulators of cell cycle progression, usually accelerate tumor progression, but their prognostic value in lung adenocarcinoma (LUAD) remains insufficiently explored.

METHODS

We collected centrosome genes from the literature and identified LUAD-specific centrosome-related genes (CRGs) using the single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA). Univariate Cox was performed to screen prognostic CRGs. Consistent clustering was performed to classify LUAD patients into two subgroups, and centrosome-related risk score signatures were constructed by Lasso and multivariate Cox regression to predict overall survival (OS). We further explored the correlation between CRS and patient prognosis, clinical manifestations, mutation status, tumor microenvironment, and response to different treatments.

RESULTS

We constructed centrosome-associated prognostic features and verified that CRS could effectively predict 1-, 3-, and 5-year survival in LUAD patients. In addition, patients in the high-risk group exhibited elevated tumor mutational loads and reduced levels of immune infiltration, particularly of T and B cells. Patients in the high-risk group were resistant to immunotherapy and sensitive to 5-fluoropyrimidine and gefitinib. The key gene spermine synthase (SRM) is highly expressed at the mRNA and protein levels in LUAD.

DISCUSSION

Our work develops a novel centrosome-related prognostic signature that accurately predicts OS in LUAD and can assist in clinical diagnosis and treatment.

DDX3X dynamics, glioblastoma's genetic landscape, therapeutic advances, and autophagic interplay

Glioblastoma is one of the most aggressive and deadly forms of cancer, posing significant challenges for the medical community. This review focuses on key aspects of Glioblastoma, including its genetic differences between primary and secondary types. Temozolomide is a major first-line treatment for Glioblastoma, and this article explores its development, how it works, and the issue of resistance that limits its effectiveness, prompting the need for new treatment strategies. Gene expression profiling has greatly advanced cancer research by revealing the molecular mechanisms of tumors, which is essential for creating targeted therapies for Glioblastoma. One important protein in this context is DDX3X, which plays various roles in cancer, sometimes promoting it or otherwise suppressing it. Additionally, autophagy, a process that maintains cellular balance, has complex implications in cancer treatment. Understanding autophagy helps to identify resistance mechanisms and potential treatments, with Chloroquine showing promise in treating Glioblastoma. This review covers the interplay between Glioblastoma, DDX3X, and autophagy, highlighting the challenges and potential strategies in treating this severe disease.

Identification of Cuproptosis-Associated Prognostic Gene Expression Signatures from 20 Tumor Types

We investigated the mRNA expression of 124 cuproptosis-associated genes in 7489 biopsies from 20 different tumor types of The Cancer Genome Atlas (TCGA). The KM plotter algorithm has been used to calculate Kaplan-Meier statistics and false discovery rate (FDR) corrections. Interaction networks have been generated using Ingenuity Pathway Analysis (IPA). High mRNA expression of 63 out of 124 genes significantly correlated with shorter survival times of cancer patients across all 20 tumor types. IPA analyses revealed that their gene products were interconnected in canonical pathways (e.g., cancer, cell death, cell cycle, cell signaling). Four tumor entities showed a higher accumulation of genes than the other cancer types, i.e., renal clear cell carcinoma (n = 21), renal papillary carcinoma (n = 13), kidney hepatocellular carcinoma (n = 13), and lung adenocarcinoma (n = 9). These gene clusters may serve as prognostic signatures for patient survival. These signatures were also of prognostic value for tumors with high mutational rates and neoantigen loads. Cuproptosis is of prognostic significance for the survival of cancer patients. The identification of specific gene signatures deserves further exploration for their clinical utility in routine diagnostics.

ENO2 in progression and treatment of colon adenocarcinoma: integrative bioinformatics analysis on non-apoptotic cell death

Colon adenocarcinoma (COAD) is one of the most common types of cancer. The interconnection between non-apoptotic cell death and COAD has not been adequately addressed. In our study, an integrative bioinformatics analysis was performed to explore non-apoptotic cell death-related biomarkers in COAD. ENO2 was determined as a potent biomarker for prognosis, drug response, immunity, and immunotherapy prediction. We used EdU and RT-qPCR assays to test our hypothesis and investigate how the ENO2 gene may influence or regulate cancer-related processes. ENO2 was expected to be a potential target in COAD.

Machine learning-based discovery of UPP1 as a key oncogene in tumorigenesis and immune escape in gliomas

Introduction

Gliomas are the most common and aggressive type of primary brain tumor, with a poor prognosis despite current treatment approaches. Understanding the molecular mechanisms underlying glioma development and progression is critical for improving therapies and patient outcomes.

Methods

The current study comprehensively analyzed large-scale single-cell RNA sequencing and bulk RNA sequencing of glioma samples. By utilizing a series of advanced computational methods, this integrative approach identified the gene UPP1 (Uridine Phosphorylase 1) as a novel driver of glioma tumorigenesis and immune evasion.

Results

High levels of UPP1 were linked to poor survival rates in patients. Functional experiments demonstrated that UPP1 promotes tumor cell proliferation and invasion and suppresses anti-tumor immune responses. Moreover, UPP1 was found to be an effective predictor of mutation patterns, drug response, immunotherapy effectiveness, and immune characteristics.

Conclusions

These findings highlight the power of combining diverse machine learning methods to identify valuable clinical markers involved in glioma pathogenesis. Identifying UPP1 as a tumor growth and immune escape driver may be a promising therapeutic target for this devastating disease.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort

Introduction

The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD.

Methods

In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD.

Results

We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma.

Discussion

Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Identification of a novel immunogenic death-associated model for predicting the immune microenvironment in lung adenocarcinoma from single-cell and Bulk transcriptomes

Background: Studies on immunogenic death (ICD) in lung adenocarcinoma are limited, and this study aimed to determine the function of ICD in LUAD and to construct a novel ICD-based prognostic model to improve immune efficacy in lung adenocarcinoma patients. Methods: The data for lung adenocarcinoma were obtained from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). The single-cell data were obtained from Bischoff P et al. To identify subpopulations, we performed descending clustering using TSNE. We collected sets of genes related to immunogenic death from the literature and identified ICD-related genes through gene set analysis of variance (GSVA) and weighted gene correlation network analysis (WGCNA). Lung adenocarcinoma patients were classified into two types using consistency clustering. The difference between the two types was analyzed to obtain differential genes. An immunogenic death model (ICDRS) was established using LASSO-Cox analysis and compared with lung adenocarcinoma models of other individuals. External validation was performed in the GSE31210 and GSE50081 cohorts. The efficacy of immunotherapy was assessed using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and immune microenvironment between different risk groups were investigated. Subsequently, ROC diagnostic curves and KM survival curves were used to screen ICDRS key regulatory genes. Finally, RT-qPCR was used to verify the differential expression of these genes. Results: Eight ICD genes were found to be highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. Additionally, ICDRS demonstrated better predictive ability compared to 11 previously published models. Furthermore, significant differences in biological function and immune cell infiltration were observed in the tumor microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both groups. These findings suggest that the model has good predictive efficacy. Conclusions: The ICD model demonstrated good predictive performance, revealing the tumor microenvironment and providing a new method for evaluating the efficacy of pre-immunization. This offers a new strategy for future treatment of lung adenocarcinoma.

DDIT3 is associated with breast cancer prognosis and immune microenvironment: an integrative bioinformatic and immunohistochemical analysis

DNA damage-inducible transcript 3 (DDIT3) is a transcription factor central to apoptosis, differentiation, and stress response. DDIT3 has been extensively studied in cancer biology. However, its precise implications in breast cancer progression and its interaction with the immune microenvironment are unclear. In this study, we utilized a novel multi-omics integration strategy, combining bulk RNA sequencing, single-cell sequencing, spatial transcriptomics and immunohistochemistry, to explore the role of DDIT3 in breast cancer and establish the correlation between DDIT3 and poor prognosis in breast cancer patients. We identified a robust prognostic signature, including six genes (unc-93 homolog B1, TLR signaling regulator, anti-Mullerian hormone, DCTP pyrophosphatase 1, mitochondrial ribosomal protein L36, nuclear factor erythroid 2, and Rho GTPase activating protein 39), associated with DDIT3. This signature stratified the high-risk patient groups, characterized by increased infiltration of the regulatory T cells and M2-like macrophages and fibroblast growth factor (FGF)/FGF receptor signaling activation. Notably, the high-risk patient group demonstrated enhanced sensitivity to immunotherapy, presenting novel therapeutic opportunities. Integrating multi-omics data helped determine the spatial expression pattern of DDIT3 in the tumor microenvironment and its correlation with immune cell infiltration. This multi-dimensional analysis provided a comprehensive understanding of the intricate interplay between DDIT3 and the immune microenvironment in breast cancer. Overall, our study not only facilitates understanding the role of DDIT3 in breast cancer but also offers innovative insights for developing prognostic models and therapeutic strategies. Identifying the DDIT3-related prognostic signature and its association with the immune microenvironment provided a promising avenue for personalized breast cancer treatment.

Dexmedetomidine attenuates acute stress-impaired learning and memory in mice by maintaining the homeostasis of intestinal flora

Dexmedetomidine (Dex) has been used in surgery to improve patients' postoperative cognitive function. However, the role of Dex in stress-induced anxiety-like behaviors and cognitive impairment is still unclear. In this study, we tested the role of Dex in anxiety-like behavior and cognitive impairment induced by acute restrictive stress and analyzed the alterations of the intestinal flora to explore the possible mechanism. Behavioral and cognitive tests, including open field test, elevated plus-maze test, novel object recognition test, and Barnes maze test, were performed. Intestinal gut Microbe 16S rRNA sequencing was analyzed. We found that intraperitoneal injection of Dex significantly improved acute restrictive stress-induced anxiety-like behavior, recognition, and memory impairment. After habituation in the environment, mice (male, 8 weeks, 18-23 g) were randomly divided into a control group (control, N = 10), dexmedetomidine group (Dex, N = 10), AS with normal saline group (AS + NS, N = 10) and AS with dexmedetomidine group (AS + Dex, N = 10). By the analysis of intestinal flora, we found that acute stress caused intestinal flora disorder in mice. Dex intervention changed the composition of the intestinal flora of acute stress mice, stabilized the ecology of the intestinal flora, and significantly increased the levels of Blautia (A genus of anaerobic bacteria) and Coprobacillus. These findings suggest that Dex attenuates acute stress-impaired learning and memory in mice by maintaining the homeostasis of intestinal flora.

Copper-binding protein modelling by single-cell transcriptome and Bulk transcriptome to predict overall survival in lung adenocarcinoma patients

Background: Copper and copper-binding proteins are key components of tumour progression as they play an important role in tumour invasion and migration, and abnormal accumulation of copper (Cu) may be intimately linked to with lung adenocarcinoma (LUAD). Methods: Data on lung adenocarcinoma were sourced from the Cancer Genome Atlas (TCGA) database and the National Centre for Biotechnology Information (GEO). 10x scRNA sequencing, which is from Bischoff P et al, was used for down-sequencing clustering and subgroup identification using TSNE. The genes for Copper-binding proteins (CBP) were acquired from the MSigDB database. LASSO-Cox analysis was subsequently used to construct a model for copper-binding proteins (CBPRS), which was then compared to lung adenocarcinoma models developed by others. External validation was carried out in the GSE31210 and GSE50081 cohorts. The effectiveness of immunotherapy was evaluated using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and the immune microenvironment between different risk groups were investigated. The CBPRS's key regulatory genes were screened using ROC diagnostic and KM survival curves. The differential expression of these genes was then verified by RT-qPCR. Results: The six CBP genes were identified as highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. The CBPRS demonstrated superior predictive ability compared to 11 previously published models. We constructed a column-line graph that includes CBPRS and clinical characteristics, which exhibits high predictive performance. Additionally, we observed significant differences in biological functions, mutational landscapes, and immune cell infiltration in the tumour microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both the high- and low-risk groups. These results suggest that the model has good predictive efficacy. Conclusions: The CBP model demonstrated good predictive performance, revealing characteristics of the tumour microenvironment. This provides a new method for assessing the efficacy of pre-immunisation and offers a potential strategy for future treatment of lung adenocarcinoma.

A PANoptosis pattern to predict prognosis and immunotherapy response in head and neck squamous cell carcinoma

Individuals diagnosed with head and neck squamous cell carcinoma (HNSCC) experience a significant occurrence rate and are susceptible to premature spreading, resulting in a bleak outlook. Therapeutic approaches, such as chemotherapy, targeted therapy, and immunotherapy, may exhibit primary and acquired resistance during the advanced phases of HNSCC. There is currently no viable solution to tackle this issue. PANoptosis-a type of non-apoptotic cell death-is a recently identified mechanism of cellular demise that entails communication and synchronization among thermal apoptosis, apoptosis, and necrosis mechanisms. However, the extent to which PANoptosis-associated genes (PRG) contribute to the forecast and immune reaction of HNSCC remains mostly undisclosed. The present study aimed to thoroughly analyze the potential importance of PRG in HNSCC and report our discoveries. We systematically analyzed 19 PRG from previous studies and clinical data from HNSCC patients to establish a PAN-related signature and assess its prognostic, predictive potential. Afterward, the patient information was separated into two gene patterns that corresponded to each other, and the analysis focused on the connection between patient prognosis, immune status, and cancer immunotherapy. The PAN score was found to correlate with survival rates, immune systems, and cancer-related pathways. We then validated the malignant role of CD27 among them in HNSCC. In summary, we demonstrated the effectiveness of PAN.Score-based molecular clustering and prognostic features in predicting the outcome of HNSCC. The discovery we made could enhance our comprehension of the significance of PAN.Score in HNSCC and facilitate the development of more effective treatment approaches.

Cancer-associated fibroblast-derived extracellular vesicles promote lymph node metastases in oral cavity squamous cell carcinoma by encapsulating ITGB1 and BMI1

BACKGROUND

Extracellular vesicles (EVs) have been revealed to facilitate the development of oral squamous cavity cell carcinoma (OCSCC), while its supporting role in lymph node metastases is under continuous investigation. This study aimed to examine the function of cancer-associated fibroblasts (CAF)-derived EVs (CAF-EVs) during lymph node metastasis in OCSCC and the mechanisms.

METHODS

CAF were isolated from OCSCC tissues of patients, and CAF-EVs were extracted and identified. EdU, colony formation, wound healing, and Transwell assays were performed. The OCSCC cells before and after CAF-EVs treatment were injected into mice to probe the effects of CAF-EVs on tumor growth and lymph node metastasis, respectively. The effect of CAF-EVs treatment on transcriptome changes in OCSCC cells was analyzed. Clinical data of patients with OCSCC were analyzed to determine the prognostic significance of the selected genes. Finally, loss-of-function assays were conducted to corroborate the involvement of polycomb complex protein BMI-1 (BMI1) and integrin beta1 (ITGB1).

RESULTS

CAF-EVs promoted the malignant behavior of OCSCC cells and accelerated tumor growth and lymph node metastasis in mice. CAF-EVs significantly increased the expression of BMI1 and ITGB1, and the expression of BMI1 and ITGB1 was negatively correlated with the overall survival and relapse-free survival of OCSCC patients. Knockdown of BMI1 or ITGB1 in OCSCC cells abated the promoting effects of CAF-EVs in vitro and in vivo.

CONCLUSION

CAF-EVs elicited the metastasis-promoting properties in OCSCC by elevating BMI1 and ITGB1, suggesting that BMI1 and ITGB1 could be potential biomarkers and therapeutic targets for OCSCC.

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.

Construction and validation of an efferocytosis-related prognostic signature in lung adenocarcinoma

BACKGROUND

Accumulating evidence highlights the potential significance of efferocytosis in tumor progression. This study is directed towards the construction of a prognostic risk model for lung adenocarcinoma (LUAD), grounded on efferocytosis-related genes (ERGs).

METHODS

Employing LASSO-COX regression analysis, a risk-prognostic model was formulated, centered on seven ERGs. Concurrently, a nomogram was established that incorporated patient clinical features and risk scores. The predictive accuracy of the risk model and the nomogram was substantiated via external validation sets. The landscapes of immune infiltration and genetic mutation were evaluated for high- and low-risk groups, with the expression of seven key genes validated through RT-PCR.

RESULTS

Our findings reveal that the high-risk group displayed considerably inferior survival outcomes in comparison to the low-risk group. A diminished abundance of immune cell infiltrates and a higher prevalence of gene mutations characterized the high-risk group. Genes with high expression were markedly enriched in pathways related to cell proliferation. The superior predictive performance of the risk model and nomogram was adequately substantiated by the external validation sets (GSE31210, GSE30219, and GSE50081). In addition, we discerned several potential therapeutic drugs demonstrating different sensitivities across patient risk groups. The differential expression of seven central genes was confirmed in A549, H1299, and BEAS-2B cell lines.

CONCLUSION

The constructed risk model and nomogram display high accuracy in predicting the survival and immune landscape of LUAD patients, thus providing invaluable prognostic tools in clinical scenarios.

Comprehensive analysis of the immunogenic cell death-related signature for predicting prognosis and immunotherapy efficiency in patients with lung adenocarcinoma

BACKGROUND

Although immunotherapy has been considered as a potent strategy for lung adenocarcinoma (LUAD), only a small part of patients was served as potentially clinical benefiters. Immunogenic cell death (ICD), a type of regulated cell death (RCD), which enable to reshape the tumor immune microenvironment and contribute to the immunotherapy efficiency. Developing a novel ICD-based signature may be a potential strategy to differentiate prognosis of patients with LUAD and predict efficacy of immunotherapy.

METHODS

In this study, 34 ICD-related genes (ICDRGs) were identified and analyzed in LUAD samples from the Cancer Genome Atlas (TCGA). 572 patients with LUAD were divided into two distinct clusters according to ICDRGs expression levels. Patients were subsequently classified into two distinct gene subtypes based on differentially expressed genes (DEGs) analyzed between two ICD-related clusters. We further developed and validated a novel ICD-related score (ICDRS) followed by comprehensive investigation about the landscape of the prognosis, immune-based features, immunotherapautic responses and sensitivity of target drugs in patients with LUAD.

RESULTS

After confirming transcriptomic aberrations and appraising prognostic value of ICDRGs, two ICD-associated subtypes were initially determined by consensus clustering in accordance with differentially expressional levels of ICDRGs. It was shown that patients in the ICD high-subtype possessed the superior clinical prognosis, abundant immune cell infiltration and higher involvement in immune-related signaling compared with the ICD low-subtype. A signature of ICD-related score (ICDRS) was further established and validated, which was served as an independent prognostic indicator for LUAD patients. These comprehensive results revealed that the high-score patients represented better clinical prognosis, higher immune infiltration-related characteristics, stronger expression of immune checkpoints, and better response to immune checkpoint inhibitor therapy and multiple targeted drugs. To further verify our analysis, we selected TLR4 as the representative of ICDRGs and evaluated its expression on the lung normal cells and cancer cells in vitro. Then, relative animal experiments were performed in vivo, with results of that the stimulation of TLR4 suppressed the growth of lung cancer.

CONCLUSIONS

In conclusion, our comprehensive analysis of ICDRGs in LUAD demonstrated their function in serving as a biomarker of predicting prognosis and clinical effects of immunotherapy and targeted drugs, which is meaningful to improve our understanding of ICDRGs and brought inspirations about evaluating prognosis and developing effective therapeutic strategies to patients with LUAD.

ATHENA: an independently validated autophagy-related epigenetic prognostic prediction model of head and neck squamous cell carcinoma

The majority of these existing prognostic models of head and neck squamous cell carcinoma (HNSCC) have unsatisfactory prediction accuracy since they solely utilize demographic and clinical information. Leveraged by autophagy-related epigenetic biomarkers, we aim to develop a better prognostic prediction model of HNSCC incorporating CpG probes with either main effects or gene-gene interactions. Based on DNA methylation data from three independent cohorts, we applied a 3-D analysis strategy to develop An independently validated auTophagy-related epigenetic prognostic prediction model of HEad and Neck squamous cell carcinomA (ATHENA). Compared to prediction models with only demographic and clinical information, ATHENA has substantially improved discriminative ability, prediction accuracy and more clinical net benefits, and shows robustness in different subpopulations, as well as external populations. Besides, epigenetic score of ATHENA is significantly associated with tumor immune microenvironment, tumor-infiltrating immune cell abundances, immune checkpoints, somatic mutation and immunity-related drugs. Taken together these results, ATHENA has the demonstrated feasibility and utility of predicting HNSCC survival ( http://bigdata.njmu.edu.cn/ATHENA/ ).

Identification of biomarkers related to prostatic hyperplasia based on bioinformatics and machine learning

In older adults, benign prostatic hyperplasia (BPH) is the most common cause of lower urinary tract symptoms (LUTS). This study aimed to explore the genes with diagnostic value in patients with BPH, reveal the relationship between the expression of diagnosis-related genes and the immune microenvironment, and provide a reference for molecular diagnosis and immunotherapy of BPH. The combined gene expression data of GSE6099, GSE7307 and GSE119195 in the GEO database were used. The differential expression of autophagy-related genes between BPH patients and healthy controls was obtained by differential analysis. Then the genes related to BPH diagnosis were screened by a machine learning algorithm and verified. Finally, five important genes (IGF1, PSIP1, SLC1A3, SLC2A1 and T1A1) were obtained by random forest (RF) algorithm, and their relationships with the immune microenvironment were discussed. Five genes play an essential role in the occurrence and development of BPH and may become new diagnostic markers of BPH. Among them, immune cells have significant correlation with some genes. The signal transduction of IL-4 mediated by M2 macrophages is closely related to the progress of BPH. There are abundant active mast cells in BPH. The adoption and metastasis of regulatory T cells may be an important method to treat BPH.

Prognostic value of autophagy-related genes based on single-cell RNA-sequencing in colorectal cancer

Background: Colorectal cancer (CRC) is the second most common cancer in China. Autophagy plays an important role in the initiation and development of CRC. Here, we assessed the prognostic value and potential functions of autophagy-related genes (ARGs) using integrated analysis using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA).

Methods: We analyzed GEO-scRNA-seq data from GEO using various single-cell technologies, including cell clustering, and identification of differentially expressed genes (DEGs) in different cell types. Additionally, we performed gene set variation analysis (GSVA). The differentially expressed ARGs among different cell types and those between CRC and normal tissues were identified using TCGA-RNA-seq data, and the hub ARGs were screened. Finally, a prognostic model based on the hub ARGs was constructed and validated, and patients with CRC in TCGA datasets were divided into high- and low-risk groups based on their risk-score, and immune cells infiltration and drug sensitivity analyses between the two groups were performed.

Results: We obtained single-cell expression profiles of 16,270 cells, and clustered them into seven types of cells. GSVA revealed that the DEGs among the seven types of cells were enriched in many signaling pathways associated with cancer development. We screened 55 differentially expressed ARGs, and identified 11 hub ARGs. Our prognostic model revealed that the 11 hub ARGs including CTSB, ITGA6, and S100A8, had a good predictive ability. Moreover, the immune cell infiltrations in CRC tissues were different between the two groups, and the hub ARGs were significantly correlated with the enrichment of immune cell infiltration. The drug sensitivity analysis revealed that the patients in the two risk groups had difference in their response to anti-cancer drugs.

Conclusion: We developed a novel prognostic 11-hub ARG risk model, and these hubs may act as potential therapeutic targets for CRC.

Pan-cancer analysis identifies proteasome 26S subunit, ATPase (PSMC) family genes, and related signatures associated with prognosis, immune profile, and therapeutic response in lung adenocarcinoma

Background: Proteasome 26S subunit, ATPase gene (PSMC) family members play a critical role in regulating protein degradation and are essential for tumor development. However, little is known about the integrative function and prognostic significance of the PSMC gene family members in lung cancer. Methods: First, we assessed the expression and prognostic features of six PSMC family members in pan-cancer from The Cancer Genome Atlas (TCGA) dataset. Hence, by focusing on the relationship between PSMC genes and the prognostic, genomic, and tumor microenvironment features in lung adenocarcinoma (LUAD), a PSMC-based prognostic signature was established using consensus clustering and multiple machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO) Cox regression, CoxBoost, and survival random forest analysis in TCGA and GSE72094. We then validated it in three independent cohorts from GEO and estimated the correlation between risk score and clinical features: genomic features (alterations, tumor mutation burden, and copy number variants), immune profiles (immune score, TIDE score, tumor-infiltrated immune cells, and immune checkpoints), sensitivity to chemotherapy (GDSC, GSE42127, and GSE14814), and immunotherapy (IMvigor210, GSE63557, and immunophenoscore). Twenty-one patients with LUAD were included in our local cohort, and tumor samples were submitted for evaluation of risk gene and PD-L1 expression. Results: Nearly all six PSMC genes were overexpressed in pan-cancer tumor tissues; however, in LUAD alone, they were all significantly correlated with overall survival. Notably, they all shared a positive association with increased TMB, TIDE score, expression of immune checkpoints (CD276 and PVR), and more M1 macrophages but decreased B-cell abundance. A PSMC-based prognostic signature was established based on five hub genes derived from the differential expression clusters of PSMC genes, and it was used to dichotomize LUAD patients into high- and low-risk groups according to the median risk score. The area under the curve (AUC) values for predicting survival at 1, 3, and 5 years in the training cohorts were all >.71, and the predictive accuracy was also robust and stable in the GSE72094, GSE31210, and GSE13213 datasets. The risk score was significantly correlated with advanced tumor, lymph node, and neoplasm disease stages as an independent risk factor for LUAD. Furthermore, the risk score shared a similar genomic and immune feature as PSMC genes, and high-risk tumors exhibited significant genomic and chromosomal instability, a higher TIDE score but lower immune score, and a decreased abundance of B and CD8⁺ T cells. Finally, high-risk patients were suggested to be less sensitive to immunotherapy but had a higher possibility of responding to platinum-based chemotherapy. The LUAD samples from the local cohort supported the difference in the expression levels of these five hub genes between tumor and normal tissues and the correlation between the risk score and PD-L1 expression. Conclusion: Overall, our results provide deep insight into PSMC genes in LUAD, especially the prognostic effect and related immune profile that may predict therapeutic responses.

LINC00669 promotes lung adenocarcinoma growth by stimulating the Wnt/β-catenin signaling pathway

Lung cancer poses severe threats to human health. It is indispensable to discover more druggable molecular targets. We identified a novel dysregulated long non-coding RNA (lncRNA), LINC00669, in lung adenocarcinoma (LUAD) by analyzing the TCGA and GEO databases. Pan-cancer analysis indicated significantly upregulated LINC00669 across 33 cancer types. GSEA revealed a tight association of LINC00669 with the cell cycle. We next attempted to improve the prognostic accuracy of this lncRNA by establishing a risk signature in reliance on cell cycle genes associated with LINC00669. The resulting risk score combined with LINC00669 and stage showed an AUC of 0.746. The risk score significantly stratified LUAD patients into low- and high-risk subgroups, independently predicting prognosis. Its performance was verified by nomogram (C-index = 0.736) and decision curve analysis. Gene set variation analysis disclosed the two groups' molecular characteristics. We also evaluated the tumor immune microenvironment by dissecting 28 infiltrated immune cells, 47 immune checkpoint gene expressions, and immunophenoscore within the two subgroups. Furthermore, the risk signature could predict sensitivity to immune checkpoint inhibitors and other anticancer therapies. Eventually, in vitro and in vivo experiments were conducted to validate LINC00669's function using qRT-PCR, CCK8, flow cytometry, western blot, and immunofluorescence staining. The gain- and loss-of-function study substantiated LINC00669's oncogenic effects, which stimulated non-small cell lung cancer cell proliferation but reduced apoptosis via activating the Wnt/β-catenin pathway. Its oncogenic potentials were validated in the xenograft mouse model. Overall, we identified a novel oncogenic large intergenic non-coding RNA (lincRNA), LINC00669. The resulting signature may facilitate predicting prognosis and therapy responses in LUAD.

Smooth muscle cell fate decisions decipher a high-resolution heterogeneity within atherosclerosis molecular subtypes

BACKGROUND

Mounting evidence has revealed the dynamic variations in the cellular status and phenotype of the smooth muscle cell (SMC) are vital for shaping the atherosclerotic plaque microenvironment and ultimately mapping onto heterogeneous clinical outcomes in coronary artery disease. Currently, the underlying clinical significance of SMC evolutions remains unexplored in atherosclerosis.

METHODS

The dissociated cells from diseased segments within the right coronary artery of four cardiac transplant recipients and 1070 bulk samples with atherosclerosis from six bulk cohorts were retrieved. Following the SMC fate trajectory reconstruction, the MOVICS algorithm integrating the nearest template prediction was used to develop a stable and robust molecular classification. Subsequently, multi-dimensional potential biological implications, molecular features, and cell landscape heterogeneity among distinct clusters were decoded.

RESULTS

We proposed an SMC cell fate decision signature (SCFDS)-based atherosclerosis stratification system and identified three SCFDS subtypes (C1-C3) with distinguishing features: (i) C1 (DNA-damage repair type), elevated base excision repair (BER), DNA replication, as well as oxidative phosphorylation status. (ii) C2 (immune-activated type), stronger immune activation, hyper-inflammatory state, the complex as well as varied lesion microenvironment, advanced stage, the most severe degree of coronary stenosis severity. (iii) C3 (stromal-rich type), abundant fibrous content, stronger ECM metabolism, immune-suppressed microenvironment.

CONCLUSIONS

This study uncovered atherosclerosis complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying SMC. The novel high-resolution stratification system could improve clinical outcomes and facilitate individualized management.

Identification and validation of a novel phagocytosis regulators-related signature with potential prognostic and immunotherapeutic value in patients with lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a malignant tumor that seriously affects the prognosis of patients. Tumor-associated macrophages (TAMs) play a vital role in the tumor microenvironment and can be used as a potential target for tumor therapy, and phagocytosis regulators (PRs) are particularly important in this process. However, the PRs-related signature that can predict the potential prognostic and immunotherapeutic value in patients with LUAD has not been discovered.

METHODS

In this study, we mainly analyzed the effect of phagocytosis regulators on the prognosis of LUAD, and based on multiple screening analyses including differential analysis, univariate Cox analysis, and Lasso analysis, we constructed a prognostic risk model consisting of five genes. To verify the stability of the model, survival analysis and ROC curve verification were carried out through multiple data sets. In addition, we also combined many factors, such as immune infiltrating cells, clinical grouping characteristics, immune examination sites, pro-inflammatory factors, and other factors as well as in vitro cell experiments and clinical tissue samples for further validation analysis.

RESULTS

After identifying 29 differentially expressed PRs in LUAD samples, we further constructed a prognostic model consisting of five prognostic signatures (FURIN, KIF23, SASH3, GNPNAT1, and ITGAL). Further survival analysis tests, ROC verification, as well as univariate and multivariate Cox regression analysis showed that the risk score of the model could well predict the prognosis of LUAD patients and could be used as an independent prognostic factor. In addition, we further found that these phagocytic regulators-related signatures were closely related to the immune microenvironment and immunotherapy in LUAD patients, and could well predict the efficacy of immunotherapy in patients. In vitro cell experiments and Immunohistochemistry of clinical tissues showed that the expressions of FURIN, KIF23, SASH3, GNPNAT1 and ITGAL in normal lung cells/tissues and LUAD cells/tissues were consistent with bioinformatics results, and 3 of them had significant differences.

CONCLUSION

Our study identified a novel PRs-related signature that has potential application value in predicting the prognosis of LUAD patients and predicting the efficacy of immunotherapy. This provides a new basis for the prognosis assessment of LUAD patients and provides a novel target for immunotherapy of LUAD patients.

Neddylation pattern indicates tumor microenvironment characterization and predicts prognosis in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer with a complex tumor microenvironment. Neddylation, as a type of post-translational modification, plays a vital role in the development of LUAD. To date, no study has explored the potential of neddylation-associated genes for LUAD classification, prognosis prediction, and treatment response evaluation.

Methods: Seventy-six neddylation-associated prognostic genes were identified by Univariate Cox analysis. Patients with LUAD were classified into two patterns based on unsupervised consensus clustering analysis. In addition, a 10-gene prognostic signature was constructed using LASSO-Cox and a multivariate stepwise regression approach.

Results: Substantial differences were observed between the two patterns of LUAD in terms of prognosis. Compared with neddylation cluster2, neddylation cluster1 exhibited low levels of immune infiltration that promote tumor progression. Additionally, the neddylation-related risk score correlated with clinical parameters and it can be a good predictor of patient outcomes, gene mutation levels, and chemotherapeutic responses.

Conclusion: Neddylation patterns can distinguish tumor microenvironment and prognosis in patients with LUAD. Prognostic signatures based on neddylation-associated genes can predict patient outcomes and guide personalized treatment.

LncRNA profiles from Notch signaling: Implications for clinical management and tumor microenvironment of colorectal cancer

The interplay between long non-coding RNAs (lncRNAs) and the Notch pathway involves a variety of malignancies. However, Notch-derived lncRNAs and their latent clinical significance remain elusive in colorectal cancer (CRC). In this study, we introduced a framework that could screen Notch-derived lncRNAs (named “NLncer”) and ultimately identified 24 NLncers. To further explore the clinical significance of these NLncers, we performed LASSO and Cox regression in TCGA-CRC cohort (n = 584) and then retained six lncRNAs tightly associated with prognosis. The final model (termed “NLncS”) was subsequently tested in GSE38832 (n = 122), GSE39582 (n = 573), and an in-house clinical cohort (n = 115). Ultimately, our NLncS model could serve as an independent risk factor and afford a robust performance for assessing the prognosis of CRC patients. Additionally, patients with high NLncS risk scores were characterized by upregulation of immune pathways, strong immunogenicity, abundant CD8 + T-cell infiltration, and potentially higher response rates to CTLA4 blockers, which turned out to be suitable for immunotherapy. Aiming at globally observing the characteristics of high-risk patients, somatic mutation and methylation modification analysis provide us with evidence at the genomic and transcriptomic levels. To facilitate the clinical transformability, we mined deeply into the sensitive compounds targeting high-risk individuals and identified dasatinib as a candidate agent for patients with a high Notch risk score. In conclusion, our NLncS model is a promising biomarker for optimizing the clinical management of CRC patients.