Integration of Single-Cell RNA Sequencing and Bulk RNA Sequencing Data to Establish and Validate a Prognostic Model for Patients With Lung Adenocarcinoma

Machine Learning-enhanced Signature of Metastasis-related T Cell Marker Genes for Predicting Overall Survival in Malignant Melanoma

In this study, we aimed to investigate disparities in the tumor immune microenvironment (TME) between primary and metastatic malignant melanoma (MM) using single-cell RNA sequencing (scRNA-seq) and to identify metastasis-related T cell marker genes (MRTMGs) for predicting patient survival using machine learning techniques. We identified 6 distinct T cell clusters in 10×scRNA-seq data utilizing the Uniform Manifold Approximation and Projection (UMAP) algorithm. Four machine learning algorithms highlighted SRGN, PMEL, GPR143, EIF4A2, and DSP as pivotal MRTMGs, forming the foundation of the MRTMGs signature. A high MRTMGs signature was found to be correlated with poorer overall survival (OS) and suppression of antitumor immunity in MM patients. We developed a nomogram that combines the MRTMGs signature with the T stage and N stage, which accurately predicts 1-year, 3-year, and 5-year OS probabilities. Furthermore, in an immunotherapy cohort, a high MRTMG signature was associated with an unfavorable response to anti-programmed death 1 (PD-1) therapy. In conclusion, primary and metastatic MM display distinct TME landscapes with different T cell subsets playing crucial roles in metastasis. The MRTMGs signature, established through machine learning, holds potential as a valuable biomarker for predicting the survival of MM patients and their response to anti-PD-1 therapy.

Single-cell analysis revealed that MTIF2 could promote hepatocellular carcinoma progression through modulating the ROS pathway

Aims

To analyze the expression of mitochondrial translational initiation factor 2 (MTIF2) and the biological functions of the gene in hepatocellular carcinoma (HCC).

Background

The treatment of HCC treatment and its prognostic prediction are limited by a lack of comprehensive understanding of the molecular mechanisms in HCC. OBJECTIVE: To determine the cells expressing MTIF2 in HCC and the function of the MTIF2+ cell subpopulation.

Methods

Gene expression analysis on TIMER 2.0, UALCAN, and GEPIA databases was performed to measure the expression of MTIF2 in HCC tissues. Cell clustering subgroups and annotation were conducted based on the single-cell sequencing data of HCC and paracancerous tissues in the Gene Expression Omnibus (GEO) database. MTIF2 expression in different cell types was analyzed. Further, biological pathways potentially regulated by MTIF2 in each cell type were identified. In addition, protein-protein interaction (PPI) networks of MTIF2 with genes in its regulated biological pathways were developed. The cell function assay was performed to verify the effects of superoxide dismutase-2 (SOD2) and MTIF2 on HCC cells. Finally, we screened virtual drugs targeting MTIF2 and SOD2 employing database screening, molecular docking and molecular dynamics.

Results

MTIF2 showed a remarkably high expression in HCC tissues. We identified a total of 10 cell types between HCC tissues and paracancerous tissues. MTIF2 expression was upregulated in epithelial cells, macrophages, and hepatocytes. More importantly, high-expressed MTIF2 in HCC tissues was mainly derived from epithelial cells and hepatocytes, in which the reactive oxygen species (ROS) pathway was significantly positively correlated with MTIF2. In the PPI network, there was a unique interaction pair between SOD2 and MTIF2 in the ROS pathway. Cell function experiments showed that overexpression of MTIF2 enhanced the proliferative and invasive capacities of HCC, which could synergize with SOD2 to co-promote the development of HCC. Finally, molecular dynamics simulations showed that DB00183 maintained a high structural stability with MTIF2 and SOD2 proteins during the simulation process.

Conclusion

Our study confirmed that the high-expressed MTIF2 in HCC tissues was derived from epithelial cells and hepatocytes. MTIF2 might act on SOD2 to regulate the ROS pathway, thereby affective the progression of HCC.

Construction and validation of prognostic signatures related to mitochondria and macrophage polarization in gastric cancer

Background

Increasing evidence reveals the involvement of mitochondria and macrophage polarisation in tumourigenesis and progression. This study aimed to establish mitochondria and macrophage polarisation-associated molecular signatures to predict prognosis in gastric cancer (GC) by single-cell and transcriptional data.

Methods

Initially, candidate genes associated with mitochondria and macrophage polarisation were identified by differential expression analysis and weighted gene co-expression network analysis. Subsequently, candidate genes were incorporated in univariateCox analysis and LASSO to acquire prognostic genes in GC, and risk model was created. Furthermore, independent prognostic indicators were screened by combining risk score with clinical characteristics, and a nomogram was created to forecast survival in GC patients. Further, in single-cell data analysis, cell clusters and cell subpopulations were yielded, followed by the completion of pseudo-time analysis. Furthermore, a more comprehensive immunological analysis was executed to uncover the relationship between GC and immunological characteristics. Ultimately, expression level of prognostic genes was validated through public datasets and qRT-PCR.

Results

A risk model including six prognostic genes (GPX3, GJA1, VCAN, RGS2, LOX, and CTHRC1) associated with mitochondria and macrophage polarisation was developed, which was efficient in forecasting the survival of GC patients. The GC patients were categorized into high-/low-risk subgroups in accordance with median risk score, with the high-risk subgroup having lower survival rates. Afterwards, a nomogram incorporating risk score and age was generated, and it had significant predictive value for predicting GC survival with higher predictive accuracy than risk model. Immunological analyses revealed showed higher levels of M2 macrophage infiltration in high-risk subgroup and the strongest positive correlation between risk score and M2 macrophages. Besides, further analyses demonstrated a better outcome for immunotherapy in low-risk patients. In single-cell and pseudo-time analyses, stromal cells were identified as key cells, and a relatively complete developmental trajectory existed for stromal C1 in three subclasses. Ultimately, expression analysis revealed that the expression trend of RGS2, GJA1, GPX3, and VCAN was consistent with the results of the TCGA-GC dataset.

Conclusion

Our findings demonstrated that a novel prognostic model constructed in accordance with six prognostic genes might facilitate the improvement of personalised prognosis and treatment of GC patients.

Integrated bioinformatics analysis reveals the bidirectional effects of TSPAN6 for cisplatin resistance in lung cancer

Cisplatin-based chemotherapy is frequently employed as the primary therapeutic approach for advanced lung cancer. Nevertheless, a significant proportion of patients may develop resistance to cisplatin, leading to diminished efficacy of chemotherapy. Through analysis of Gene Expression Omnibus databases, TSPAN6 has been identified as a key factor in conferring resistance to cisplatin, attributed to its activation of the NF-κB signaling pathway. Knockdown of TSPAN6 using siRNA resulted in decreased expression levels of NF-κB in A549 cells. This indicates that TSPAN6 may have dual effects on lung cancer cisplatin resistance and could serve as a promising therapeutic target for individuals with cisplatin resistance.

A single-cell characterised signature integrating heterogeneity and microenvironment of lung adenocarcinoma for prognostic stratification

BACKGROUND

The high heterogeneity of tumour and the complexity of tumour microenvironment (TME) greatly impacted the tumour development and the prognosis of cancer in the era of immunotherapy. In this study, we aimed to portray the single cell-characterised landscape of lung adenocarcinoma (LUAD), and develop an integrated signature incorporating both tumour heterogeneity and TME for prognosis stratification.

METHODS

Single-cell tagged reverse transcription sequencing (STRT-seq) was performed on tumour tissues and matched normal tissues from 14 patients with LUAD for immune landscape depiction and candidate key genes selection for signature construction. Kaplan-Meier survival analyses and in-vitro cell experiments were conducted to confirm the gene functions. The transcriptomic profile of 1949 patients from 11 independent cohorts including nine public datasets and two in-house cohorts were obtained for validation.

FINDINGS

We selected 11 key genes closely related to cell-to-cell interaction, tumour development, T cell phenotype transformation, and Ma/Mo cell distribution, including HLA-DPB1, FAM83A, ITGB4, OAS1, FHL2, S100P, FSCN1, SFTPD, SPP1, DBH-AS1, CST3, and established an integrated 11-gene signature, stratifying patients to High-Score or Low-Score group for better or worse prognosis. Moreover, the prognostically-predictive potency of the signature was validated by 11 independent cohorts, and the immunotherapeutic predictive potency was also validated by our in-house cohort treated by immunotherapy. Additionally, the in-vitro cell experiments and drug sensitivity prediction further confirmed the gene function and generalizability of this signature across the entire RNA profile spectrum.

INTERPRETATION

This single cell-characterised 11-gene signature might offer insights for prognosis stratification and potential guidance for treatment selection.

FUNDING

Support for the study was provided by National key research and development project (2022YFC2505004, 2022YFC2505000 to Z.W. and J.W.), Beijing Natural Science Foundation (7242114 to J.X.), National Natural Science Foundation of China of China (82102886 to J.X., 81871889 and 82072586 to Z.W.), Beijing Nova Program (20220484119 to J.X.), NSFC general program (82272796 to J.W.), NSFC special program (82241229 to J.W.), CAMS Innovation Fund for Medical Sciences (2021-1-I2M-012, 2022-I2M-1-009 to Z.W. and J.W.), Beijing Natural Science Foundation (7212084 to Z.W.), CAMS Key lab of translational research on lung cancer (2018PT31035 to J.W.), Aiyou Foundation (KY201701 to J.W.). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022003 to J.X.).

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.

Development and validation of a disulfidptosis-related scoring system to predict clinical outcome and immunotherapy response in acute myeloid leukemia by integrated analysis of single-cell and bulk RNA-sequencing

Background: Disulfidptosis is a metabolically relevant mode of cell death, and its relationship with acute myeloid leukemia (AML) has not been clarified. In this study, disulfidptosis scores were computed to examine the potential biological mechanisms. Methods: Consensus clustering was applied to detect disulfidptosis-related molecular subtypes. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a DRG prognostic model. Results: DRGs are upregulated in AML and associated with poor prognosis. The higher the disulfidptosis activity score, the worse the clinical outcome for patients, accompanied by increased immune checkpoint expression and tumor marker pathway activity. The two molecular subtypes exhibited distinct prognoses and tumor microenvironment (TME) profiles. A prognostic risk score model was established using six DRGs, and the AML cohort was divided into high- and low-risk score groups. Patients in the high-risk group experienced significantly worse prognosis, which was validated in seven AML cohorts. Receiver Operating Characteristic (ROC) curve analysis indicated that the area under the curve values for risk score prediction of 1-, 3-, and 5-year survival were 0.779, 0.714, and 0.778, respectively. The nomogram, in conjunction with clinicopathological factors, further improved the accuracy of prognosis prediction. The high-risk score group exhibited a higher somatic mutation frequency, increased immune-related signaling pathway activity, and greater immune checkpoint expression, suggesting a certain degree of immunosuppression. Patients with advanced age and higher cytogenetic risk also had elevated risk scores. According to drug prediction and AML anti-PD-1 therapy cohort analysis, the low-risk score group displayed greater sensitivity to chemotherapy drugs like cytarabine and midostaurin, while the high-risk score group was more responsive to anti-PD-1 therapy. Finally, clinical samples were collected for sequencing analysis, confirming that the progression of myeloid leukemia was associated with a higher risk score and a negative disulfidptosis score, suggesting that the poor prognosis of AML may be associated with disulfidptosis resistance. Conclusion: In conclusion, a systematic analysis of DRGs can help to identify potential disulfidptosis-related mechanisms and provide effective new biomarkers for prognosis prediction, TME assessment, and the establishment of personalized treatment plans in AML.

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

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

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and immunotherapy response in bladder cancer

BACKGROUND

T cells have been proven to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in bladder cancer (BLCA).

METHODS

Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the gene expression omnibus (GEO) database to screen T-cell marker genes. Bulk RNA-sequencing data and clinical information from BLCA patients were downloaded from the cancer genome atlas (TCGA) database to develop a prognosis signature. We analyzed the association of different risk groups with survival analysis, gene set enrichment analysis (GSEA), tumor mutational burden (TMB), and immunotherapy response.

RESULTS

Based on 192 T-cell marker genes identified by scRNA-seq analysis, we constructed a prognostic signature containing 7 genes in the training cohort, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5 years were 0.734, 0.742 and 0.726 in the training cohort, 0.697, 0.671 and 0.670 in the testing cohort, 0.702, 0.665 and 0.629 in the GEO cohort, respectively. In addition, we constructed a nomogram based on clinical factors and the risk score of the signature. The low-risk group exhibited higher immune-related pathways, immune cell infiltration and TMB levels. Importantly, immunophenotype score and immunotherapy cohort (IMvigor210) analyses showed that the low-risk group had better immunotherapy response and prognosis.

CONCLUSIONS

Our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for BLCA patients.

Identification of immunotherapy biomarkers for improving the clinical outcome of homologous recombination deficiency patients with lung adenocarcinoma

Homologous recombination deficiency (HRD) is a common molecular signature of genomic instability and has been shown to be a biomarker for targeted therapies. However, there is a lack of studies on the role of HRD changes in lung adenocarcinoma (LUAD) transcriptomics. HRD scores were determined using single nucleotide polymorphism (SNP) array data from LUAD patients from The Cancer Genome Atlas (TCGA) database. Transcriptional data from patients with different scores were analyzed to identify biomarkers associated with HRD. Candidate biomarkers were validated using Gene Expression Omnibus (GEO)-sourced datasets and an immunotherapy cohort. According to the bulk transcriptome and clinical characteristics of 912 LUAD patients and Single-cell RNA-seq of 9 LUAD patients from TCGA and GEO databases, we observed increased MS4A6A expression in HRD tumors; high MS4A6A expression predicted improved survival outcomes. Furthermore, a comprehensive analysis of the tumor immune microenvironment (TIME) revealed a positive correlation between MS4A6A expression and neoantigen loading and immune cell infiltration. Additionally, the immunotherapy cohort confirmed the possibility of using MS4A6A as a biomarker. Collectively, we suggest that MS4A6A is associated with HRD and provide a new perspective toward identifying promising biomarkers for immunotherapy.

Single-cell and bulk RNA sequencing identifies T cell marker genes score to predict the prognosis of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, with limited biomarkers identified to predict its prognosis and treatment response of immune checkpoint blockade (ICB). This study aimed to explore the predictive ability of T cell marker genes score (TMGS) to predict their overall survival (OS) and treatment response to ICB by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Multi-omics data of PDAC were applied in this study. The uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification. The non-negative matrix factorization (NMF) algorithm was applied to molecular subtypes clustering. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was adopted for TMGS construction. The prognosis, biological characteristics, mutation profile, and immune function status between different groups were compared. Two molecular subtypes were identified via NMF: proliferative PDAC (C1) and immune PDAC (C2). Distinct prognoses and biological characteristics were observed between them. TMGS was developed based on 10 T cell marker genes (TMGs) through LASSO-Cox regression. TMGS is an independent prognostic factor of OS in PDAC. Enrichment analysis indicated that cell cycle and cell proliferation-related pathways are significantly enriched in the high-TMGS group. Besides, high-TMGS is related to more frequent KRAS, TP53, and CDKN2A germline mutations than the low-TMGS group. Furthermore, high-TMGS is significantly associated with attenuated antitumor immunity and reduced immune cell infiltration compared to the low-TMGS group. However, high TMGS is correlated to higher tumor mutation burden (TMB), a low expression level of inhibitory immune checkpoint molecules, and a low immune dysfunction score, thus having a higher ICB response rate. On the contrary, low TMGS is related to a favorable response rate to chemotherapeutic agents and targeted therapy. By combining scRNA-seq and bulk RNA-seq data, we identified a novel biomarker, TMGS, which has remarkable performance in predicting the prognosis and guiding the treatment pattern for patients with PDAC.

Therapeutic Strategies and Oncological Outcome of Peritoneal Metastases from Lung Cancer: A Systematic Review and Pooled Analysis

The peritoneum is an unusual site of metastases from lung cancer, and optimal management at the moment remains unclear and mostly based on palliative strategies. Therefore, the aim of the study was to investigate demographic characteristics, management and overall survival of patients with peritoneal metastases from lung cancer (PCLC). A PRISMA-compliant systematic review and pooled analysis was performed searching all English studies published until December 2022. PROSPERO, CRD42022349362. Inclusion criteria were original articles including patients with peritoneal carcinomatosis from lung cancer, specifying at least one outcome of interest. Exclusion criteria were being unable to retrieve patient data from articles, and the same patient series included in different studies. Among 1746 studies imported for screening, twenty-one were included (2783 patients). Mean overall survival was between 0.5 and 5 months after peritoneal carcinomatosis diagnosis and 9 and 21 months from lung cancer diagnosis. In total, 27% of patients underwent first-line or palliative chemotherapy and 7% of them surgery. Management differs significantly among published studies. The literature on PCLC is scarce. Its incidence is low but appears to be substantially rising and is likely to be an underestimation. Prognosis is very poor and therapeutic strategies have been limited and used in a minority of patients. Subcategories of PCLC patients may have an improved prognosis and may benefit from an aggressive oncological approach, including cytoreductive surgery. Further investigation would be needed in this regard.

Comprehensive molecular characterizations of stage I-III lung adenocarcinoma with tumor spread through air spaces

Purpose: The aim of this study is to investigate integrative genomic spectra of stage I-III lung adenocarcinoma with tumor spread through air spaces (STAS). Methods: We retrospectively identified 442 surgically resected lung adenocarcinoma patients of pathological stage I-III in Shanghai Chest Hospital from January 2018 to February 2021. Surgically resected tissues were used for next-generation sequencing (NGS) with a panel of 68 lung cancer-related genes to profile comprehensive molecular characterizations. Results: A total of 442 cases were analyzed, including 221 (50%) STAS-positive (SP) and 221 (50%) STAS-negative (SN) lung adenocarcinoma patients. In total, 440 cases (99.6%) were positive for the overall mutational spectrum, and the higher mutational genes were EGFR, TP53, KRAS, ALK, SMAD4, and ERBB2 (62%, 42%, 14%, 10%, 7%, and 7%, respectively). Compared with the SN population, there was significantly lower EGFR alteration in the single-nucleotide variant (SNV) mutation spectrum (52.5% vs 69.7%, p < 0.001) and significantly higher TP53 alteration in the SP population (49.8% vs 34.8%, p = 0.002). EGFR L858R missense mutation (19.5% vs 37.6%, p < 0.001) and ERBB2 exon 20 indel mutation (1.8% vs 5.9%, p = 0.045) were more frequent in the SN population. The detection rate of ALK fusion rearrangements in the SP population was significantly higher than that in the SN population (13.1% vs 2.3%, p < 0.001). In the analysis of signaling pathways, no significant difference was discovered between SP and SN patients. No difference in 1-year disease-free survival was observed between SP and SN patients in this study. Conclusion: Significant differences exist in stage I-III lung adenocarcinoma patients with STAS in molecular characterizations.

The role of PD-1/PD-L1 axis in idiopathic pulmonary fibrosis: Friend or foe?

Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease with a bleak prognosis. Mounting evidence suggests that IPF shares bio-molecular similarities with lung cancer. Given the deep understanding of the programmed cell death-1 (PD-1)/programmed death-ligand 1 (PD-L1) pathway in cancer immunity and the successful application of immune checkpoint inhibitors (ICIs) in lung cancer, recent studies have noticed the role of the PD-1/PD-L1 axis in IPF. However, the conclusions are ambiguous, and the latent mechanisms remain unclear. In this review, we will summarize the role of the PD-1/PD-L1 axis in IPF based on current murine models and clinical studies. We found that the PD-1/PD-L1 pathway plays a more predominant profibrotic role than its immunomodulatory role in IPF by interacting with multiple cell types and pathways. Most preclinical studies also indicated that blockade of the PD-1/PD-L1 pathway could attenuate the severity of pulmonary fibrosis in mice models. This review will bring significant insights into understanding the role of the PD-1/PD-L1 pathway in IPF and identifying new therapeutic targets.

A novel enterocyte-related 4-gene signature for predicting prognosis in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a fatal disease, and its cases are constantly increasing worldwide. Further, the therapeutic and management strategies for patients with COAD are still unsatisfactory due to the lack of accurate patient classification and prognostic models. Therefore, our study aims to identify prognostic markers in patients with COAD and construct a cell subtype-specific prognostic model with high accuracy and robustness.

Methods

Single-cell transcriptomic data of six samples were retrieved from the Gene expression omnibus (GEO) database. The cluster annotation and cell-cell communication analysis identified enterocytes as a key player mediating signal communication networks. A four-gene signature prognostic model was constructed based on the enterocyte-related differentially expressed genes (ERDEGs) in patients with COAD of the Cancer Genome Atlas cohort. The prognostic model was validated on three external validation cohorts from the GEO database. The correlation between immune cell infiltration, immunotherapy response, drug sensitivity, and the four-gene signature prognostic model was investigated. Finally, immunohistochemistry (IHC) was performed to determine the expression of the four genes.

Results

We found that the proportion of epithelial cells was obviously large in COAD samples. Further analysis of epithelial cells showed that the activity of the enterocytes was highest in the cell-cell communication network. Based on enterocyte data, 30 ERDEGs were identified and a 4-gene prognostic model including CPM, CLCA4, ELOVL6, and ATP2A3 was developed and validated. The risk score derived from this model was considered as an independent variable factor to predict overall survival. The patients were divided into high- and low-risk groups based on the median riskscore value. The correlation between immune cell infiltration, immunotherapy response, immune status, clinical characteristics, drug sensitivity, and risk score was analyzed. IHC confirmed the expression of signature genes in tissues from normal individuals, patients with polyps, and COAD.

Conclusion

In this study, we constructed and validated a novel four-gene signature prognostic model, which could effectively predict the response to immunotherapy and overall survival in patients with COAD. More importantly, this model provides useful insight into the management of colon cancer patients and aids in designing personalized therapy.

Development and validation of a prognostic model and gene co-expression networks for breast carcinoma based on scRNA-seq and bulk-seq data

Background

Breast carcinoma is the most common malignancy among women worldwide. It is characterized by a complex tumor microenvironment (TME), in which there is an intricate combination of different types of cells, which can cause confusion when screening tumor-cell-related signatures or constructing a gene co-expression network. The recent emergence of single-cell RNA sequencing (scRNA-seq) is an effective method for studying the changing omics of cells in complex TMEs.

Methods

The Dysregulated genes of malignant epithelial cells was screened by performing a comprehensive analysis of the public scRNA-seq data of 58 samples. Co-expression and Gene Set Enrichment Analysis (GSEA) analysis were performed based on scRNA-seq data of malignant cells to illustrate the potential function of these dysregulated genes. Iterative LASSO-Cox was used to perform a second-round screening among these dysregulated genes for constructing risk group. Finally, a breast cancer prognosis prediction model was constructed based on risk grouping and other clinical characteristics.

Results

Our results indicated a transcriptional signature of 1,262 genes for malignant breast cancer epithelial cells. To estimate the function of these genes in breast cancer, we also constructed a co-expression network of these dysregulated genes at single-cell resolution, and further validated the results using more than 300 published transcriptomics datasets and 31 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening datasets. Moreover, we developed a reliable predictive model based on the scRNA-seq and bulk-seq datasets.

Conclusions

Our findings provide insights into the transcriptomics and gene co-expression networks during breast carcinoma progression and suggest potential candidate biomarkers and therapeutic targets for the treatment of breast carcinoma. Our results are available via a web app (https://prognosticpredictor.shinyapps.io/GCNBC/).

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and therapeutic response in lung squamous cell carcinoma

Cancer immunotherapy is an increasingly successful strategy for treating patients with advanced or conventionally drug-resistant cancers. T cells have been proved to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in lung squamous cell carcinoma (LUSC). In this study, we first performed a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data from the gene expression omnibus (GEO) database to identify 72 T-cell marker genes. Subsequently, we constructed a 5-gene prognostic signature in the training cohort based on the T-cell marker genes from the cancer genome atlas (TCGA) database, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5-years were 0.614, 0.713 and 0.702 in the training cohort, 0.669, 0.603 and 0.645 in the testing cohort, 0.661, 0.628 and 0.590 in the GEO cohort, respectively. Furthermore, we created a highly reliable nomogram to facilitate clinical application. Gene set enrichment analysis showed that immune-related pathways were mainly enriched in the high-risk group. Tumor immune microenvironment indicated that high-risk group exhibited higher immune score, stromal score, and immune cell infiltration levels. Moreover, genes of the immune checkpoints and human leukocyte antigen family were all overexpressed in high-risk group. Drug sensitivity revealed that low-risk group was sensitive to 8 chemotherapeutic drugs and high-risk group to 4 chemotherapeutic drugs. In short, our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for LUSC patients.

Integrated analysis of bulk and single-cell RNA-seq reveals the role of MYC signaling in lung adenocarcinoma

MYC is one of the well-known oncogenes, and its important role in cancer still remains largely unknown. We obtained lung adenocarcinoma (LUAD) multi-omics data including genome, transcriptome, and single-cell sequencing data from multiple cohorts. We calculated the GSVA score of the MYC target v1 using the ssGSEA method, and obtained the genes highly correlated with this score by Spearman correlation analysis. Subsequent hierarchical clustering divided these genes into two gene sets highly associated with MYC signaling (S1 and S2). Unsupervised clustering based on these genes divided the LUAD samples into two distinct subgroups, namely, the MYC signaling inhibition group (C1) and activation group (C2). The MCP counter package in R was used to assess tumor immune cell infiltration abundance and ssGSEA was used to calculate gene set scores. The scRNA-seq was used to verify the association of MYC signaling to cell differentiation. We observed significant differences in prognosis, clinical characteristics, immune microenvironment, and genomic alterations between MYC signaling inhibition and MYC signaling activation groups. MYC-signaling is associated with genomic instability and can mediate the immunosuppressive microenvironment and promote cell proliferation, tumor stemness. Moreover, MYC-signaling activation is also subject to complex post-transcriptional regulation and is highly associated with cell differentiation. In conclusion, MYC signaling is closely related to the genomic instability, genetic alteration and regulation, the immune microenvironment landscape, cell differentiation, and disease survival in LUAD. The findings of this study provide a valuable reference to revealing the mechanism of cancer-promoting action of MYC in LUAD.