A Novel Immune-Related Prognostic Model for Response to Immunotherapy and Survival in Patients With Lung Adenocarcinoma

Value of the HOTAIR expression assay in predicting therapy target in hepatocellular carcinoma: A meta-analysis and bioinformatics analysis

BACKGROUND

Several studies show that the long non-coding RNA HOX transcript antisense RNA (HOTAIR) was upregulated in human cancer, which was associated with several clinical features and may have the potential to be prognostic markers. However, the significance of HOTAIR in hepatocellular carcinoma remains unclear. We performed a meta-analysis and bioanalysis to further investigate the association between HOTAIR and hepatocellular carcinoma.

METHODS

Eligible literature was systematically retrieved from PubMed, Embase, and Web of Science databases. The pooled hazard ratios with 95% confidence intervals were used to evaluate to the effect. Raw data on HOTAIR expression were obtained from The Cancer Genome Atlas data portals. All bioinformatics analyses were performed using R software (version 4.3.1).

RESULTS

We identified eight studies in this meta-analysis with a total of 399 patients. High-level HOTAIR expression was found to be significantly related to advanced tumor node metastasis stage, distant metastasis, poor tumor differentiation, and patients with hepatitis. Correspondingly, HOTAIR was also associated with poor overall survival and relapse-free survival. Subsequently, in bioanalysis, HOTAIR expression was higher in hepatocellular carcinoma as well as poor overall survival. High HOTAIR expression was strongly correlated with tumor node metastasis stage. Kyoto Encyclopedia of Genes and Genomes analysis revealed that the differentially expressed genes related to HOTAIR may be involved in the cancer-associated signaling pathway.

CONCLUSION

HOTAIR may be a potential biomarker for HCC prediction and is expected to become a new choice for clinical HCC prediction..

G0 arrest gene patterns to predict the prognosis and drug sensitivity of patients with lung adenocarcinoma

G0 arrest (G0A) is widely recognized as a crucial factor contributing to tumor relapse. The role of genes related to G0A in lung adenocarcinoma (LUAD) was unclear. This study aimed to develop a gene signature based on for LUAD patients and investigate its relationship with prognosis, tumor immune microenvironment, and therapeutic response in LUAD. We use the TCGA-LUAD database as the discovery cohort, focusing specifically on genes associated with the G0A pathway. We used various statistical methods, including Cox and lasso regression, to develop the model. We validated the model using bulk transcriptome and single-cell transcriptome datasets (GSE50081, GSE72094, GSE127465, GSE131907 and EMTAB6149). We used GSEA enrichment and the CIBERSORT algorithm to gain insight into the annotation of the signaling pathway and the characterization of the tumor microenvironment. We evaluated the response to immunotherapy, chemotherapy, and targeted therapy in these patients. The expression of six genes was validated in cell lines by quantitative real-time PCR (qRT-PCR). Our study successfully established a six-gene signature (CHCHD4, DUT, LARP1, PTTG1IP, RBM14, and WBP11) that demonstrated significant predictive power for overall survival in patients with LUAD. It demonstrated independent prognostic value in LUAD. To enhance clinical applicability, we developed a nomogram based on this gene signature, which showed high reliability in predicting patient outcomes. Furthermore, we observed a significant association between G0A-related risk and tumor microenvironment as well as drug susceptibility, highlighting the potential of the gene signature to guide personalized treatment strategies. The expression of six genes were significantly upregulated in the LUAD cell lines. This signature holds the potential to contribute to improved prognostic prediction and new personalized therapies specifically for LUAD patients.

The GJB3 correlates with the prognosis, immune cell infiltration, and therapeutic responses in lung adenocarcinoma

Gap junction protein beta 3 (GJB3) has been reported as a tumor suppressor in most tumors. However, its role in lung adenocarcinoma (LUAD) remains unknown. The purpose of this study is to explore the role of GJB3 in the prognosis and tumor microenvironment of LUAD patients. The data used in this study were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and imvigor210 cohorts. We found that GJB3 expression was increased in LUAD patients and correlated with LUAD stages. LUAD patients with high GJB3 expression exhibited a worse prognosis. A total of 164 pathways were significantly activated in the GJB3 high group. GJB3 expression was positively associated with nine transcription factors and might be negatively regulated by hsa-miR-6511b-5p. Finally, we found that immune cell infiltration and immune checkpoint expression were different between the GJB3 high and GJB3 low groups. In summary. GJB3 demonstrated high expression levels in LUAD patients, and those with elevated GJB3 expression displayed unfavorable prognoses. Additionally, there was a correlation between GJB3 and immune cell infiltration, as well as immune checkpoint expression in LUAD patients.

A novel m7G regulator-based methylation patterns in head and neck squamous cell carcinoma

Abnormal RNA N7-methylguanosine (m7G) modification is known to contribute to effects on tumor occurrence and development. Nevertheless, the mechanisms of its function in immunoregulation, tumor microenvironment (TME) modulation, and tumor promotion remain largely unknown. A series of computer-aided bioinformatic analyses were conducted based on transcriptomic, single-cell sequence, and spatial transcriptomic data to determine the m7G modification patterns in head and neck squamous cell carcinoma (HNSCC). Consensus clustering approach was employed according to the expressions of 33 m7G regulators. ESTIMATE, CIBERSORT, and single sample gene set enrichment analysis algorithms were adopted to investigate the immune cell infiltration features. A prognostic model named m7Gscore was established. Seurat, SingleR, and Monocle2 were used to analyze the single-cell sequence profiling. STUtility was used to integrate multiple spatial transcriptomic datasets. Quantitative reverse transcription polymerase chain reaction, transwell, and wound-healing assay were performed to verify the oncogenes. Here, three different m7G modification patterns were highlighted in HNSCC patients, which were also related to various clinical manifestations and three representative immunophenotypes: immune-excluded, immune-desert, and inflamed, separately. Patients with lower m7Gscore were highlighted by higher immune cell infiltrations, better overall survival rates, lesser tumor mutation burden (TMB), lower sensitivities to target inhibitors therapies, and better immunotherapeutic response. Moreover, DCPS, EIF4E, EIF4E2, LSM1, NCBP2, NUDT1, and NUDT5 were identified to play critical roles in T-cell differentiation. Knockdown of LSM1/NUDT5 could restrain the malignancy of HNSCC cells. Collectively, quantitative assessment of m7G modification patterns in individual HNSCC patients could contribute to identifying more efficient immunotherapeutic approaches and improve the clinical outcome of HNSCC.

Neurotransmitter receptor-related gene signature as potential prognostic and therapeutic biomarkers in colorectal cancer

Background: Colorectal cancer is one of the most common malignant tumors worldwide. A various of neurotransmitter receptors have been found to be expressed in tumor cells, and the activation of these receptors may promote tumor growth and metastasis. This study aimed to construct a novel neurotransmitter receptor-related genes signature to predict the survival, immune microenvironment, and treatment response of colorectal cancer patients. Methods: RNA-seq and clinical data of colorectal cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Neurotransmitter receptor-related gene were collected from publicly available data sources. The Weighted Gene Coexpression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms were employed to construct the Neurotransmitter receptor-related gene prognostic signature. Further analyses, functional enrichment, CIBERSORTx, The Tumor Immune Single Cell Center (TISCH), survival analysis, and CellMiner, were performed to analyze immune status and treatment responses. Quantitative real-time polymerase chain reaction (qRT-PCR) assays were carried out to confirm the expression levels of prognostic genes. Results: By combining machine learning algorithm and WGCNA, we identified CHRNA3, GABRD, GRIK3, and GRIK5 as Neurotransmitter receptor-related prognostic genes signature. Functional enrichment analyses showed that these genes were enriched with cellular metabolic-related pathways, such as organic acid, inorganic acid, and lipid metabolism. CIBERSORTx and Single cell analysis showed that the high expression of genes were positively correlated with immunosuppressive cells infiltration, and the genes were mainly expressed in cancer-associated fibroblasts and endothelial cells. A nomogram was further built to predict overall survival (OS). The expression of CHRNA3, GABRD, GRIK3, and GRIK5 in cancer cells significantly impacted their response to chemotherapy. Conclusion: A neurotransmitter receptor-related prognostic gene signature was developed and validated in the current study, giving novel sights of neurotransmitter in predicting the prognostic and improving the treatment of CRC.

Drug resistance related genes in lung adenocarcinoma predict patient prognosis and influence the tumor microenvironment

Lung adenocarcinoma (LUAD) is the predominant type of non-small lung cancer (NSCLC) with strong invasive ability and poor prognosis. The drug resistance related genes are potentially associated with prognosis of LUAD. Our research aimed to identify the drug resistance related genes and explore their potential prognostic value in LUAD patients. The data used in this study were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Firstly, we screened drug resistance related genes in LUAD by differential gene analysis, univariate Cox regression and drug sensitivity analyses. Subsequently, we constructed a risk score model using LASSO Cox regression analysis, and verified whether the risk score can predict the survival of LUAD patients independent of other factors. Moreover, we explored the immune infiltration of 22 immune cells between high-risk and low-risk patients. Totally 10 drug-resistance positively related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) were identified in LUAD. The risk score model of LUAD constructed with these 10 genes could reliably predict the prognosis of LUAD patients. 18 pathways were significantly activated in high-risk group compared with low-risk group. In addition, the infiltration proportion of multiple immune cells was significantly different between high-risk and low-risk groups, and the proportion of M1 phagocytes was significantly higher in the high-risk group compared with the low-risk group. The drug resistance related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) could predict the prognosis of LUAD patients. Clarifying the roles and mechanisms of these 10 genes in regulating drug resistance in LUAD will help to improve individualized clinical treatment protocols and predict patient sensitivity to treatment.

5-Methylcytosine-related lncRNAs: predicting prognosis and identifying hot and cold tumor subtypes in head and neck squamous cell carcinoma

BACKGROUND

5-Methylcytosine (m⁵C) methylation is recognized as an mRNA modification that participates in biological progression by regulating related lncRNAs. In this research, we explored the relationship between m⁵C-related lncRNAs (mrlncRNAs) and head and neck squamous cell carcinoma (HNSCC) to establish a predictive model.

METHODS

RNA sequencing and related information were obtained from the TCGA database, and patients were divided into two sets to establish and verify the risk model while identifying prognostic mrlncRNAs. Areas under the ROC curves were assessed to evaluate the predictive effectiveness, and a predictive nomogram was constructed for further prediction. Subsequently, the tumor mutation burden (TMB), stemness, functional enrichment analysis, tumor microenvironment, and immunotherapeutic and chemotherapeutic responses were also assessed based on this novel risk model. Moreover, patients were regrouped into subtypes according to the expression of model mrlncRNAs.

RESULTS

Assessed by the predictive risk model, patients were distinguished into the low-MLRS and high-MLRS groups, showing satisfactory predictive effects with AUCs of 0.673, 0.712, and 0.681 for the ROCs, respectively. Patients in the low-MLRS groups exhibited better survival status, lower mutated frequency, and lower stemness but were more sensitive to immunotherapeutic response, whereas the high-MLRS group appeared to have higher sensitivity to chemotherapy. Subsequently, patients were regrouped into two clusters: cluster 1 displayed immunosuppressive status, but cluster 2 behaved as a hot tumor with a better immunotherapeutic response.

CONCLUSIONS

Referring to the above results, we established a m⁵C-related lncRNA model to evaluate the prognosis, TME, TMB, and clinical treatments for HNSCC patients. This novel assessment system is able to precisely predict the patients' prognosis and identify hot and cold tumor subtypes clearly for HNSCC patients, providing ideas for clinical treatment.

Necrotic related-lncRNAs: Prediction of prognosis and differentiation between cold and hot tumors in head and neck squamous cell carcinoma

Treatment of head and neck squamous cell carcinoma (HNSCC) is a substantial clinical challenge due to the high local recurrence rate and chemotherapeutic resistance. This project seeks to identify new potential biomarkers of prognosis prediction and precision medicine to improve this condition. A synthetic data matrix for RNA transcriptome datasets and relevant clinical information on HNSCC and normal tissues was downloaded from the Genotypic Tissue Expression Project and The Cancer Genome Atlas (TCGA). The necrosis-associated long-chain noncoding RNAs (lncRNAs) were identified by Pearson correlation analysis. Then 8-necrotic-lncRNA models in the training, testing and entire sets were established through univariate Cox (uni-Cox) regression and Lasso-Cox regression. Finally, the prognostic ability of the 8-necrotic-lncRNA model was evaluated via survival analysis, nomogram, Cox regression, clinicopathological correlation analysis, and receiver operating characteristic (ROC) curve. Gene enrichment analysis, principal component analysis, immune analysis and prediction of risk group semi-maximum inhibitory concentration (IC50) were also conducted. Correlations between characteristic risk score and immune cell infiltration, immune checkpoint molecules, somatic gene mutations, and anti-cancer drug sensitivity were analyzed. Eight necrosis-associated lncRNAs (AC099850.3, AC243829.2, AL139095.4, SAP30L-AS1, C5orf66-AS1, LIN02084, LIN00996, MIR4435-2HG) were developed to improve the prognosis prediction of HNSCC patients. The risk score distribution, survival status, survival time, and relevant expression standards of these lncRNAs were compared between low- and high-risk groups in the training, testing and entire sets. Kaplan-Meier analysis showed the low-risk patients had significantly better prognosis. The ROC curves revealed the model had an acceptable predictive value in the TCGA training and testing sets. Cox regression and stratified survival analysis indicated that the 8 necrosis-associated lncRNAs were risk factors independent of various clinical parameters. We recombined the patients into 2 clusters through Consensus ClusterPlus R package according to the expressions of necrotic lncRNAs. Significant differences were found in immune cell infiltration, immune checkpoint molecules, and IC50 between clusters, suggesting these characteristics can be used to evaluate the clinical efficacy of chemotherapy and immunotherapy. This risk model may serve as a prognostic signature and provide clues for individualized immunotherapy for HNSCC patients.

Radiomics combined with transcriptomics to predict response to immunotherapy from patients treated with PD-1/PD-L1 inhibitors for advanced NSCLC

Introduction

In this study, we aim to build radiomics and multiomics models based on transcriptomics and radiomics to predict the response from patients treated with the PD-L1 inhibitor.

Materials and methods

One hundred and ninety-five patients treated with PD-1/PD-L1 inhibitors were included. For all patients, 342 radiomic features were extracted from pretreatment computed tomography scans. The training set was built with 110 patients treated at the Léon Bérard Cancer Center. An independent validation cohort was built with the 85 patients treated in Dijon. The two sets were dichotomized into two classes, patients with disease control and those considered non-responders, in order to predict the disease control at 3 months. Various models were trained with different feature selection methods, and different classifiers were evaluated to build the models. In a second exploratory step, we used transcriptomics to enrich the database and develop a multiomic signature of response to immunotherapy in a 54-patient subgroup. Finally, we considered the HOT/COLD status. We first trained a radiomic model to predict the HOT/COLD status and then prototyped a hybrid model integrating radiomics and the HOT/COLD status to predict the response to immunotherapy.

Results

Radiomic signature for 3 months' progression-free survival (PFS) classification: The most predictive model had an area under the receiver operating characteristic curve (AUROC) of 0.94 on the training set and 0.65 on the external validation set. This model was obtained with the t-test selection method and with a support vector machine (SVM) classifier. Multiomic signature for PFS classification: The most predictive model had an AUROC of 0.95 on the training set and 0.99 on the validation set. Radiomic model to predict the HOT/COLD status: the most predictive model had an AUROC of 0.93 on the training set and 0.86 on the validation set. HOT/COLD radiomic hybrid model for PFS classification: the most predictive model had an AUROC of 0.93 on the training set and 0.90 on the validation set.

Conclusion

In conclusion, radiomics could be used to predict the response to immunotherapy in non-small-cell lung cancer patients. The use of transcriptomics or the HOT/COLD status, together with radiomics, may improve the working of the prediction models.

Genetic variants in the calcium signaling pathway participate in the pathogenesis of colorectal cancer through the tumor microenvironment

Background

Cancer risk is influenced by calcium signaling in intracellular and intercellular signaling pathways. However, the relationship between the calcium signaling pathway and colorectal cancer risk remains unknown. We aim to evaluate the role of genetic variants in calcium signaling pathway genes in colorectal cancer risk through the tumor microenvironment.

Methods

An analysis of genetic variants in the calcium signaling pathway was conducted using a case-control study that included 1150 colorectal cancer patients and 1342 non-cancer patients. Using the regression model, we assessed whether single-nucleotide polymorphisms (SNPs) increase the risk of colorectal cancer. We also performed a dual luciferase reporter gene assay using HCT116 cell lines and DLD1 cell lines to demonstrate the regulatory relationship between SNP and candidate risk gene. We evaluated the expression of candidate risk gene in different populations. In addition, we also evaluated candidate risk gene and 22 immune cells correlation studies.

Results

There was a significant association between the PDE1C rs12538364 T allele and colorectal cancer risk [odds ratio (OR) = 1.57, 95% confidence interval (CI) = 1.30 - 1.90, P = 3.07 × 10^-6, P _FDR = 0.004]. Mutation of intron region rs1538364 C to T locus reduces promoter activity of PDE1C in DLD1 and HCT116 cell lines (P < 0.05). We identified that PDE1C is significantly down-regulated in colorectal cancer, closely associated with 22 immune cells. Finally, we found that PDE1C could be the biomarker for individual immunotherapy of colorectal cancer.

Conclusion

According to our findings, PDE1C may be a key factor contributing to colorectal cancer, thus improving individual immunotherapy for the disease. The potential mechanism by which polymorphisms in the calcium signaling pathway genes may participate in the pathogenesis of colorectal cancer through the tumor microenvironment.

A network-based dynamic criterion for identifying prediction and early diagnosis biomarkers of complex diseases

Lung adenocarcinoma (LUAD) seriously threatens human health and generally results from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (IEWNS) is proposed for improving the performance of LUAD early diagnosis. To this end, we theoretically derived a dynamic criterion, namely, the relationship of variation (RV), to construct dynamic networks. RV infers correlation [Formula: see text] statistics to measure dynamic changes in molecular relationships during the process of disease development. Based on the dynamic networks constructed by IEWNS, network warning signals used to represent the occurrence of LUAD deterioration can be defined without human intervention. IEWNS was employed to perform a comprehensive analysis of gene expression profiles of LUAD from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The experimental results suggest that the potential biomarkers selected by IEWNS can facilitate a better understanding of pathogenetic mechanisms and help to achieve effective early diagnosis of LUAD. In conclusion, IEWNS provides novel insight into the initiation and progression of LUAD and helps to define prospective biomarkers for assessing disease deterioration.

Comprehensive analysis of necroptosis-related long noncoding RNA to predict prognosis, immune status, and immunotherapeutic response in clear cell renal cell carcinoma

Background

Necroptosis has been found to be associated with tumorigenesis and tumor progression. However, the prognostic effect of long noncoding RNAs (lncRNAs) associated with necroptosis in clear cell renal cell carcinoma (ccRCC) is still unclear.

Methods

Pearson correlation analysis was used to identify necroptosis-related genes and lncRNAs obtained from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression analyses were used to identify a novel necroptosis-associated lncRNAs signature that significantly correlated with survival of ccRCC. Next, single sample gene set enrichment analysis (ssGSEA) was employed to assess the extent of infiltration with immune cells. Analyses to predict the half-maximal inhibitory concentration (IC50) of patients in different risk groups were also conducted. Moreover, follow-up data of an immunotherapy cohort were used to test for differences in the immunotherapeutic efficiency between two risk groups. Finally, patients with ccRCC were divided into two groups based on 6 prognostic lncRNAs.

Results

We developed a signature of necroptosis-related lncRNAs, which was verified as an independent prognostic factor that can predict prognosis up to 7 years. Patients with higher risk scores were shown to have higher immune suppressive cell infiltration levels and expression of immune checkpoint genes, which suggests that these patients were in a state of immunosuppression. Patients in the low-risk group were found to have an increased response to immunotherapy. A prognostic prediction nomogram was conducted to predict long-term survival of patients. Cluster A tumors were considered hot tumors, since they were correlated with higher levels of immune infiltration and were more sensitive to immunotherapy.

Conclusions

A comprehensive bioinformatics analysis was conducted, which found that the necroptosis-associated lncRNA signature might be a potent prognostic factor for patients with ccRCC, which could contribute to improved prognosis of these patients.

Immunotherapy for lung cancer combining the oligodeoxynucleotides of TLR9 agonist and TGF-β2 inhibitor

Tumor immunotherapies have shown promising antitumor effects, especially immune checkpoint inhibitors (ICIs). However, only 12.46% of the patients benefit from the ICIs, the rest of them shows limited effects on ICIs or even accelerates the tumor progression due to the lack of the immune cell infiltration and activation in the tumor microenvironment (TME). In this study, we administrated a combination of Toll-like receptor 9 (TLR9) agonist CpG ODN and Transforming growth factor-β2 (TGF-β2) antisense oligodeoxynucleotide TIO3 to mice intraperitoneally once every other day for a total of four injections, and the first injection was 24 h after LLC cell inoculation. We found that the combination induced the formation of TME toward the enrichment and activation of CD8⁺ T cells and NK cells, accompanied with a marked decrease of TGF-β2. The combined therapy also effectively inhibited the tumor growth and prolonged the survival of the mice, even protected the tumor-free mice from the tumor re-challenge. Both of CpG ODN and TIO3 are indispensable, because replacing CpG ODN with TLR9 inhibitor CCT ODN showed no antitumor effect, CpG ODN or TIO3 alone did not lead to ideal antitumor results. This effect was possibly initiated by the activation of dendritic cells at the tumor site. This systemic antitumor immunotherapy with a combination of the two oligonucleotides (an immune stimulant and an immunosuppressive cytokine inhibitor) before the tumor formation may provide a novel strategy for clinical prevention of the postoperative tumor recurrence.

A novel LUAD prognosis prediction model based on immune checkpoint-related lncRNAs

Lung adenocarcinoma (LUAD) is a malignant disease with an extremely poor prognosis, and there is currently a lack of clinical methods for early diagnosis and precise treatment and management. With the deepening of tumor research, more and more attention has been paid to the role of immune checkpoints (ICP) and long non-coding RNAs (lncRNAs) regulation in tumor development. Therefore, this study downloaded LUAD patient data from the TCGA database, and finally screened 14 key ICP-related lncRNAs based on ICP-related genes using univariate/multivariate COX regression analysis and LASSO regression analysis to construct a risk prediction model and corresponding nomogram. After multi-dimensional testing of the model, the model showed good prognostic prediction ability. In addition, to further elucidate how ICP plays a role in LUAD, we jointly analyzed the immune microenvironmental changes in LAUD patients and performed a functional enrichment analysis. Furthermore, to enhance the clinical significance of this study, we performed a sensitivity analysis of common antitumor drugs. All the above works aim to point to new directions for the treatment of LUAD.

Transcriptome profiles of fatty acid metabolism-related genes and immune infiltrates identify hot tumors for immunotherapy in cutaneous melanoma

Background: Immunotherapy with checkpoint inhibitors usually has a low response rate in some cutaneous melanoma (CM) cases due to its cold nature. Hence, identification of hot tumors is important to improve the immunotherapeutic efficacy and prognoses of CMs.

Methods: Fatty acid (FA) metabolism-related genes were extracted from the Gene Set Enrichment Analysis and used in the non-negative matrix factorization (NMF), copy number variation frequency, tumor mutation burden (TMB), and immune-related analyses, such as immunophenoscore (IPS). We generate a risk model and a nomogram for predicting patient prognoses and predicted the potential drugs for therapies using the Connectivity Map. Moreover, the NMF and the risk model were validated in a cohort of cases in the GSE65904 and GSE54467. At last, immunohistochemistry (IHC) was used for further validation.

Results: Based on the NMF of 11 FA metabolism-related DEGs, CM cases were stratified into two clusters. Cluster 2 cases had the characteristics of a hot tumor with higher immune infiltration levels, higher immune checkpoint (IC) molecules expression levels, higher TMB, and more sensitivity to immunotherapy and more potential immunotherapeutic drugs and were identified as hot tumors for immunotherapy. The risk model and nomogram displayed excellent predictor values. In addition, there were more small potential molecule drugs for therapies of CM patients, such as ambroxol. In immunohistochemistry (IHC), we could find that expression of PLA2G2D, ACOXL, and KMO was upregulated in CM tissues, while the expression of IL4I1, BBOX1, and CIDEA was reversed or not detected.

Conclusion: The transcriptome profiles of FA metabolism-related genes were effective for distinguishing CM into hot–cold tumors. Our findings may be valuable for development of effective immunotherapy for CM patients and for proposing new therapy strategies.

Development and Validation of a DNA Methylation-related Classifier of Circulating Tumour Cells to Predict Prognosis and to provide a therapeutic strategy in Lung Adenocarcinoma

Background: A significant factor influencing the prognosis of lung adenocarcinoma (LUAD) is tumor metastasis. Studies have shown that abnormal DNA methylation in circulating tumor cells (CTCs) is associated with tumour metastasis. Based on the genes expressed in CTCs that play an important role in DNA methylation, we hope to build a risk model to predict prognosis and provide a therapeutic strategy in LUAD.

Methods: The CTC sequencing data for LUAD were obtained from GSE74639, which contains 10 CTC samples and 6 primary tumour samples. To carefully assess the clinical value, functional status, involvement of the tumor microenvironment (TME) based on the risk model, and genetic variants based on based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), a reliable risk model was successfully built.

Results: Three differentially methylated genes (DMGs) of CTCs for LUAD, including mitochondrial ribosomal protein L51 (MRPL51), STE20-like kinase (SLK), and protein regulator of cytokinesis 1(PRC1), were effectively used to construct a risk model. Both the training and validation cohorts' stability and accuracy of the risk model were evaluated. Each patient in the TCGA-LUAD cohort received a risk score, and based on the median score, they were divided into high- and low-risk groups. The tumors in the high-risk group in this study were classified as "cold" and immunosuppressed, which may be linked to a poor prognosis. The tumors in the low-risk group, however, were deemed "hot" and had immune hyperfunction linked to a positive prognosis. Additionally, patients in the low-risk group showed greater sensitivity to immunotherapy than those in the high-risk group.

Conclusions: Based on DMGs of CTCs from LUAD, we successfully developed a predictive risk model and discovered differences in biological function, TME, genetic variation, and clinical outcomes between those at high and low risk group.

Identification of a glycolysis- and lactate-related gene signature for predicting prognosis, immune microenvironment, and drug candidates in colon adenocarcinoma

Background: Colon adenocarcinoma (COAD), a malignant gastrointestinal tumor, has the characteristics of high mortality and poor prognosis. Even in the presence of oxygen, the Warburg effect, a major metabolic hallmark of almost all cancer cells, is characterized by increased glycolysis and lactate fermentation, which supports biosynthesis and provides energy to sustain tumor cell growth and proliferation. However, a thorough investigation into glycolysis- and lactate-related genes and their association with COAD prognosis, immune cell infiltration, and drug candidates is currently lacking.

Methods: COAD patient data and glycolysis- and lactate-related genes were retrieved from The Cancer Genome Atlas (TCGA) and Gene Set Enrichment Analysis (GSEA) databases, respectively. After univariate Cox regression analysis, a nonnegative matrix factorization (NMF) algorithm was used to identify glycolysis- and lactate-related molecular subtypes. Least absolute shrinkage and selection operator (LASSO) Cox regression identified twelve glycolysis- and lactate-related genes (ADTRP, ALDOB, APOBEC1, ASCL2, CEACAM7, CLCA1, CTXN1, FLNA, NAT2, OLFM4, PTPRU, and SNCG) related to prognosis. The median risk score was employed to separate patients into high- and low-risk groups. The prognostic efficacy of the glycolysis- and lactate-related gene signature was assessed using Kaplan–Meier (KM) survival and receiver operating characteristic (ROC) curve analyses. The nomogram, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) were employed to improve the clinical applicability of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on differentially expressed genes (DEGs) from the high- and low-risk groups. Using CIBERSORT, ESTIMATE, and single-sample GSEA (ssGSEA) algorithms, the quantities and types of tumor-infiltrating immune cells were assessed. The tumor mutational burden (TMB) and cytolytic (CYT) activity scores were calculated between the high- and low-risk groups. Potential small-molecule agents were identified using the Connectivity Map (cMap) database and validated by molecular docking. To verify key core gene expression levels, quantitative real-time polymerase chain reaction (qRT–PCR) assays were conducted.

Results: We identified four distinct molecular subtypes of COAD. Cluster 2 had the best prognosis, and clusters 1 and 3 had poor prognoses. High-risk COAD patients exhibited considerably poorer overall survival (OS) than low-risk COAD patients. The nomogram precisely predicted patient OS, with acceptable discrimination and excellent calibration. GO and KEGG pathway enrichment analyses of DEGs revealed enrichment mainly in the “glycosaminoglycan binding,” “extracellular matrix,” “pancreatic secretion,” and “focal adhesion” pathways. Patients in the low-risk group exhibited a larger infiltration of memory CD4+ T cells and dendritic cells and a better prognosis than those in the high-risk group. The chemotherapeutic agent sensitivity of patients categorized by risk score varied significantly. We predicted six potential small-molecule agents binding to the core target of the glycolysis- and lactate-related gene signature. ALDOB and APOBEC1 mRNA expression was increased in COAD tissues, whereas CLCA1 and OLFM4 mRNA expression was increased in normal tissues.

Conclusion: In summary, we identified molecular subtypes of COAD and developed a glycolysis- and lactate-related gene signature with significant prognostic value, which benefits COAD patients by informing more precise and effective treatment decisions.

A 13-gene signature to predict the prognosis and immunotherapy responses of lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) comprises 20–30% of all lung cancers. Immunotherapy has significantly improved the prognosis of LUSC patients; however, only a small subset of patients responds to the treatment. Therefore, we aimed to develop a novel multi-gene signature associated with the immune phenotype of the tumor microenvironment for LUSC prognosis prediction. We stratified the LUSC patients from The Cancer Genome Atlas dataset into hot and cold tumor according to a combination of infiltration status of immune cells and PD-L1 expression level. Kaplan–Meier analysis showed that hot tumors were associated with shorter overall survival (OS). Enrichment analyses of differentially expressed genes (DEGs) between the hot and cold tumors suggested that hot tumors potentially have a higher immune response ratio to immunotherapy than cold tumors. Subsequently, hub genes based on the DEGs were identified and protein–protein interactions were constructed. Finally, we established an immune-related 13-gene signature based on the hub genes using the least absolute shrinkage and selection operator feature selection and multivariate cox regression analysis. This gene signature divided LUSC patients into high-risk and low-risk groups and the former inclined worse OS than the latter. Multivariate cox proportional hazard regression analysis showed that the risk model constructed by the 13 prognostic genes was an independent risk factor for prognosis. Receiver operating characteristic curve analysis showed a moderate predictive accuracy for 1-, 3- and 5-year OS. The 13-gene signature also performed well in four external cohorts (three LUSC and one melanoma cohorts) from Gene Expression Omnibus. Overall, in this study, we established a reliable immune-related 13-gene signature that can stratify and predict the prognosis of LUSC patients, which might serve clinical use of immunotherapy.

Identification of cuproptosis-related subtypes and characterization of the tumor microenvironment landscape in head and neck squamous cell carcinoma

BACKGROUND

Cuproptosis is considered a novel copper-dependent cell death model. In this study, we established a novel scoring system based on 10 cuproptosis-related genes (CRGs) to predict the prognosis and immune landscape of head and neck squamous cell carcinoma (HNSCC).

METHODS

The RNA-seq data of HNSCC patients were downloaded from the GEO and TCGA databases and were merged into a novel HNSCC cohort. Multiomics landscape analyses were conducted, including tumor mutation burden (TMB), copy number variations and the interaction network of CRGs. Patients were then divided into different cuproptosis subtypes based on the expression of 10 CRGs and subsequently regrouped into novel gene clusters referring to differentially expressed genes. A cuproptosis score (CS) system was established using principal component analysis. The CIBERSORT, ssGSEA and ESTIMATE algorithms were used to assess the tumor immune microenvironment. Moreover, the immunotherapeutic and chemotherapeutic responses were assessed.

RESULTS

Patients were divided into three cuproptosis subtypes and subsequently regrouped into three gene clusters, reflecting different immune infiltration. Assessed by the CS system, those with higher CSs exhibited worse prognosis and higher TMB frequency. Nevertheless, the immune-related analysis revealed patients in the low-CS group appeared immunosuppressive and easily suffered from immune escape. High CSs possibly show high expression of immune checkpoint genes and enhance chemotherapy sensitivity to cisplatin, docetaxel, and gemcitabine.

CONCLUSION

We established a novel scoring system to predict the prognosis and immune landscape of HNSCC patients. This signature exhibits satisfactory predictive effects and the potential to guide comprehensive treatment for patients.

Assessing the Prognostic Capability of Immune-Related Gene Scoring Systems in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the commonest of the subtypes of lung cancer histologically. For this study, we intended to analyze the expression profiling of the immune-related genes (IRGs) from an independently available public database and developed a potent signature predictive of patients' prognosis.

Methods

Gene expression profiles and the clinical data of lung adenocarcinoma were gathered from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), and the obtained data were split into a training set (n = 226), test set (n = 83), and validation set (n = 400). IRGs were then gathered from the ImmPort database. A prognostic model was constructed by analyzing the training set. Then the GO and KEGG analysis was performed, and a gene correlation prognostic nomogram was constructed. Finally, external validation, such as immune infiltration and immunohistochemistry, was performed.

Results

The 110 genes were significant by univariate Cox regression analysis and randomized survival forest algorithm for the training set and showed a good distinction between the low-risk-score and high-risk-score groups in the training set (P < 0.0001) by screening for four prognosis-related genes (HMOX1, ARRB1, ADM, PDIA3) and validated by the test set GSE30219 (P=0.0025) and TCGA dataset (P=0.00059). Multivariate Cox showed that the four gene signatures were an individual risk factor for LUAD. In addition, the genes in the signatures were externally verified using an online database. In particular, PDIA3 and HMOX1 are essential genes in the prognostic nomogram and play an important role in the model of immune-related genes.

Conclusion

Four immune-related genetic signatures are reliable prognostic indicators for patients with LUAD, providing a relevant theoretical basis and therapeutic rationale for immunotherapy.

N7-methylguanosine-related lncRNAs: Predicting the prognosis and diagnosis of colorectal cancer in the cold and hot tumors

Background: 7-Methylguanosine(m7G) contributes greatly to its pathogenesis and progression in colorectal cancer. We proposed building a prognostic model of m7G-related LncRNAs. Our prognostic model was used to identify differences between hot and cold tumors.

Methods: The study included 647 colorectal cancer patients (51 cancer-free patients and 647 cancer patients) from The Cancer Genome Atlas (TCGA). We identified m7G-related prognostic lncRNAs by employing the univariate Cox regression method. Assessments were conducted using univariate Cox regression, multivariate Cox regression, receiver operating characteristics (ROC), nomogram, calibration curves, and Kaplan-Meier analysis. All of these procedures were used with the aim of confirming the validity and stability of the model. Besides these two analyses, we also conducted half-maximal inhibitory concentration (IC50), immune analysis, principal component analysis (PCA), and gene set enrichment analysis (GSEA). The entire set of m7G-related (lncRNAs) with respect to cold and hot tumors has been divided into two clusters for further discussion of immunotherapy.

Results: The risk model was constructed with 17 m7G-related lncRNAs. A good correlation was found between the calibration plots and the prognosis prediction in the model. By assessing IC50 in a significant way across risk groups, systemic treatment can be guided. By using clusters, it may be possible to distinguish hot and cold tumors effectively and to aid in specific therapeutic interventions. Cluster 1 was identified as having the highest response to immunotherapy drugs and thus was identified as the hot tumor.

Conclusion: This study shows that 17 m7G-related lncRNA can be used in clinical settings to predict prognosis and use them to determine whether a tumor is cold or hot in colorectal cancer and improve the individualization of treatment.

Development of a Risk Predictive Model for Evaluating Immune Infiltration Status in Invasive Thyroid Carcinoma

Aims

This study aimed to reveal the molecular characteristics and potential biomarker of immune-activated and immunosuppressive invasive thyroid carcinoma.

Methods

Expression and clinical data for invasive thyroid carcinoma were obtained from the TCGA database. Tumor samples were divided into immune-activated or immunosuppressive groups based on the immune enrichment score calculated by ssGSEA. Differentially expressed genes (DEGs) between tumor vs. normal groups or between immune-activated vs. immunosuppressive groups were screened, followed by functional enrichment. Immune infiltration was evaluated using the ESTIMATE, CIBERSORTx, and EPIC algorithms, respectively. A random forest algorithm and Lasso cox analysis were used to identify gene signatures for risk model construction.

Results

Totally 1171 DEGs were screened between tumor vs. normal groups, and multiple tumorigenesis-associated pathways were significantly activated in invasive thyroid carcinoma. Compared to immune-activated samples, immunosuppressive samples showed higher tumor purity, lower immune/stromal scores, and lower expression of immune markers, as well as lower infiltration abundance of CD4+ T cells and CD8+ T cells. A risk model based on a 12-immune signature (CCR7, CD1B, CD86, CSF2RB, HCK, HLA-DQA1, LTA, LTB, LYZ, NOD2, TNFRSF9, and TNFSF11) was developed to evaluate the immune infiltration status (AUC = 0.998; AUC of 0.958 and 0.979 in the two external validation datasets), which showed a higher clinical benefit and high accuracy. Immune-activated samples presented lower IC50 value for bortezomib, MG.132, staurosporine, and AZD8055, indicating sensitivity to these drugs.

Conclusion

A 12-gene-based immune signature was developed to predict the immune infiltration status for invasive thyroid carcinoma patients and then to identify the subsets of invasive thyroid carcinoma patients who might benefit from immunotherapy.

Construction of a necroptosis-related lncRNA signature to predict the prognosis and immune microenvironment of head and neck squamous cell carcinoma

Background

Previous studies have determined that necroptosis‐related genes are potential biomarkers in head and neck squamous cell carcinoma (HNSCC). Herein, we established a novel risk model based on necroptosis‐related lncRNAs (nrlncRNAs) to predict the prognosis of HNSCC patients.

Methods

Transcriptome and related information were obtained from TCGA database, and an nrlncRNA signature was established based on univariate Cox analysis and least absolute shrinkage and selection operator Cox regression. Kaplan–Meier analysis and time‐dependent receiver operating characteristic (ROC) analysis were used to evaluate the model, and a nomogram for survival prediction was established. Gene set enrichment analysis, immune analysis, drug sensitivity analysis, correlation with N6‐methylandenosin (m6A), and tumor stemness analysis were performed. Furthermore, the entire set was divided into two clusters for further discussion.

Results

A novel signature was established with six nrlncRNAs. The areas under the ROC curves (AUCs) for 1‐, 3‐, and 5‐year overall survival (OS) were 0.699, 0.686, and 0.645, respectively. Patients in low‐risk group and cluster 2 had a better prognosis, more immune cell infiltration, higher immune function activity, and higher immune scores; however, patients in high‐risk group and cluster 1 were more sensitive to chemotherapy. Moreover, the risk score had negative correlation with m6A‐related gene expression and tumor stemness.

Conclusion

According to this study, we constructed a novel signature with nrlncRNA pairs to predict the survival of HNSCC patients and guide immunotherapy and chemotherapy. This may possibly promote the development of individualized and precise treatment for HNSCC patients.

Establishment of a Prognostic Model of Lung Adenocarcinoma Based on Tumor Heterogeneity

Lung cancer is one of the main cancer types due to its persistently high incidence and mortality, yet a simple and effective prognostic model is still lacking. This study aimed to identify independent prognostic genes related to the heterogeneity of lung adenocarcinoma (LUAD), generate a prognostic risk score model, and construct a nomogram in combination with other pathological characteristics to predict patients’ overall survival (OS). A significant amount of data pertaining to single-cell RNA sequencing (scRNA-seq), RNA sequencing (RNA-seq), and somatic mutation were used for data mining. After statistical analyses, a risk scoring model was established based on eight independent prognostic genes, and the OS of high-risk patients was significantly lower than that of low-risk patients. Interestingly, high-risk patients were more sensitive and effective to immune checkpoint blocking therapy. In addition, it was noteworthy that CCL20 not only affected prognosis and differentiation of LUAD but also led to poor histologic grade of tumor cells. Ultimately, combining risk score, clinicopathological information, and CCL20 mutation status, a nomogram with good predictive performance and high accuracy was established. In short, our research established a prognostic model that could be used to guide clinical practice based on the constantly updated big multi-omics data. Finally, this analysis revealed that CCL20 may become a potential therapeutic target for LUAD.

Therapeutic Implications of Tumor Microenvironment in Lung Cancer: Focus on Immune Checkpoint Blockade

In the last decade, the treatment of non-small cell lung cancer (NSCLC) has been revolutionized by the introduction of immune checkpoint inhibitors (ICI) directed against programmed death protein 1 (PD-1) and its ligand (PD-L1), or cytotoxic T lymphocyte antigen 4 (CTLA-4). In spite of these improvements, some patients do not achieve any benefit from ICI, and inevitably develop resistance to therapy over time. Tumor microenvironment (TME) might influence response to immunotherapy due to its prominent role in the multiple interactions between neoplastic cells and the immune system. Studies investigating lung cancer from the perspective of TME pointed out a complex scenario where tumor angiogenesis, soluble factors, immune suppressive/regulatory elements and cells composing TME itself participate to tumor growth. In this review, we point out the current state of knowledge involving the relationship between tumor cells and the components of TME in NSCLC as well as their interactions with immunotherapy providing an update on novel predictors of benefit from currently employed ICI or new therapeutic targets of investigational agents. In first place, increasing evidence suggests that TME might represent a promising biomarker of sensitivity to ICI, based on the presence of immune-modulating cells, such as Treg, myeloid derived suppressor cells, and tumor associated macrophages, which are known to induce an immunosuppressive environment, poorly responsive to ICI. Consequently, multiple clinical studies have been designed to influence TME towards a pro-immunogenic state and subsequently improve the activity of ICI. Currently, the mostly employed approach relies on the association of "classic" ICI targeting PD-1/PD-L1 and novel agents directed on molecules, such as LAG-3 and TIM-3. To date, some trials have already shown promising results, while a multitude of prospective studies are ongoing, and their results might significantly influence the future approach to cancer immunotherapy.

Comprehensive analysis identifies IFI16 as a novel signature associated with overall survival and immune infiltration of skin cutaneous melanoma

Background

Skin cutaneous melanoma (SKCM) is the most common skin tumor with high mortality. The unfavorable outcome of SKCM urges the discovery of prognostic biomarkers for accurate therapy. The present study aimed to explore novel prognosis-related signatures of SKCM and determine the significance of immune cell infiltration in this pathology.

Methods

Four gene expression profiles (GSE130244, GSE3189, GSE7553 and GSE46517) of SKCM and normal skin samples were retrieved from the GEO database. Differentially expressed genes (DEGs) were then screened, and the feature genes were identified by the LASSO regression and Boruta algorithm. Survival analysis was performed to filter the potential prognostic signature, and GEPIA was used for preliminary validation. The area under the receiver operating characteristic curve (AUC) was obtained to evaluate discriminatory ability. The Gene Set Variation Analysis (GSVA) was performed, and the composition of the immune cell infiltration in SKCM was estimated using CIBERSORT. At last, paraffin-embedded specimens of primary SKCM and normal skin tissues were collected, and the signature was validated by fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC).

Results

Totally 823 DEGs and 16 feature genes were screened. IFI16 was identified as the signature associated with overall survival of SKCM with a great discriminatory ability (AUC > 0.9 for all datasets). GSVA noticed that IFI16 might be involved in apoptosis and ultraviolet response in SKCM, and immune cell infiltration of IFI16 was evaluated. At last, FISH and IHC both validated the differential expression of IFI16 in SKCM.

Conclusions

In conclusion, our comprehensive analysis identified IFI16 as a signature associated with overall survival and immune infiltration of SKCM, which may play a critical role in the occurrence and development of SKCM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02409-6.

Necroptosis-Related lncRNAs: Predicting Prognosis and the Distinction between the Cold and Hot Tumors in Gastric Cancer

Background

In the face of poor prognosis and immunotherapy failure of gastric cancer (GC), this project tried to find new potential biomarkers for predicting prognosis and precision medication to ameliorate the situation.

Methods

To form synthetic matrices, we retrieved stomach adenocarcinoma transcriptome data from Genotype-Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA). Necroptosis-related prognostic lncRNA was identified by coexpression analysis and univariate Cox regression. Then we performed the least absolute shrinkage and selection operator (LASSO) to construct the necroptosis-related lncRNA model. Next, the Kaplan–Meier analysis, time-dependent receiver operating characteristics (ROC), univariate Cox (uni-Cox) regression, multivariate Cox (multi-Cox) regression, nomogram, and calibration curves were made to verify and evaluate the model. Gene set enrichment analyses (GSEA), principal component analysis (PCA), immune analysis, and prediction of the half-maximal inhibitory concentration (IC50) in risk groups were also analyzed. For further discussing immunotherapy between the cold and hot tumors, we divided the entire set into two clusters based on necroptosis-related lncRNAs.

Results

We constructed a model with 16 necroptosis-related lncRNAs. In the model, we found the calibration plots showed a good concordance with the prognosis prediction. The area's 1-, 2-, and 3-year OS under the ROC curve (AUC) were 0.726, 0.763, and 0.770, respectively. Risk groups could be a guide of systemic treatment because of significantly different IC50 between risk groups. Above all, clusters could help distinguish between the cold and hot tumors effectively and contribute to precise mediation. Cluster 2 was identified as the hot tumor and more susceptible to immunotherapeutic drugs.

Conclusion

The results of this project supported that necroptosis-related lncRNAs could predict prognosis and help make a distinction between the cold and hot tumors for improving individual therapy in GC.

Macrophage-Related SPP1 as a Potential Biomarker for Early Lymph Node Metastasis in Lung Adenocarcinoma

Lymph node metastasis is a major factor that affects prognosis in patients with lung adenocarcinoma (LUAD). In some cases, lymph node metastasis has already occurred when the primary tumors are still small (i.e., early T stages), however, relevant studies on early lymph node metastasis are limited, and effective biomarkers remain lacking. This study aimed to explore new molecular biomarker for early lymph node metastasis in LUAD using transcriptome sequencing and experimental validation. Here, we performed transcriptome sequencing on tissues from 16 matched patients with Stage-T1 LUAD (eight cases of lymph node metastasis and eight cases of non-metastasis), and verified the transcriptome profiles in TCGA, GSE68465, and GSE43580 cohorts. With the bioinformatics analysis, we identified a higher abundance of M0 macrophages in the metastatic group using the CIBERSORT algorithm and immunohistochemistry (IHC) analysis and the enrichment of the epithelial–mesenchymal transition (EMT) pathway was identified in patients with higher M0 infiltration levels. Subsequently, the EMT hallmark gene SPP1, encoding secreted phosphoprotein 1 (SPP1), was identified to be significantly correlated with macrophage infiltration and M2 polarization, and was determined to be a key risk indicator for early lymph node metastasis. Notably, SPP1 in the blood, as detected by enzyme-linked immunosorbent assay (ELISA) showed a superior predictive capability for early lymph node metastasis [area under the curve (AUC) = 0.74]. Furthermore, a long non-coding RNA (lncRNA, AC037441), negatively correlated with SPP1 and macrophage infiltration, had also been identified and validated to be involved in the regulation of early lymph node metastasis. In conclusion, we revealed the potential role of macrophages in lymph node metastasis and identified the macrophage-related gene SPP1 as a potential biomarker for early lymph node metastasis in LUAD.

Identification of prognostic markers by weighted gene co-expression network analysis in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the fatal tumors and is associated with a poor prognosis. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to quantify the proportions of 22 types of immune cells. Weighted gene co-expression network analysis (WGCNA) was established from the GSE37745 data, and key modules correlating most with CD8⁺ T cell infiltration were determined. Genes that manifested a high module connectivity in the key module were identified as hub genes. Three bioinformatics online databases were used to evaluate hub gene expression levels in tumor and normal tissues. Finally, survival analysis was conducted for these hub genes. In this study, we chose four hub genes (AURKB, CDC20, TPX2 and KIF2C) based on the comprehensive bioinformatics analyses. All hub genes were overexpressed in tumor tissue, and high expression of AURKB, CDC20, TPX2, and KIF2C correlated with the poor prognosis of these patients. In vitro experiments confirmed that CDC20 knockdown inhibited cell proliferation and growth. The above results indicated that AURKB, CDC20, TPX2, and KIF2C are potential CD8⁺ T cell infiltration-related biomarkers and therapeutic targets.