Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

Co-Expression of Multiple PAX Genes in Renal Cell Carcinoma (RCC) and Correlation of High PAX Expression with Favorable Clinical Outcome in RCC Patients

Renal cell carcinoma (RCC) is the most common form of kidney cancer, consisting of multiple distinct subtypes. RCC has the highest mortality rate amongst the urogenital cancers, with kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), and kidney chromophobe carcinoma (KICH) being the most common subtypes. The Paired-box (PAX) gene family encodes transcription factors, which orchestrate multiple processes in cell lineage determination during embryonic development and organogenesis. Several PAX genes have been shown to be expressed in RCC following its onset and progression. Here, we performed real-time quantitative polymerase chain reaction (RT-qPCR) analysis on a series of human RCC cell lines, revealing significant co-expression of PAX2, PAX6, and PAX8. Knockdown of PAX2 or PAX8 mRNA expression using RNA interference (RNAi) in the A498 RCC cell line resulted in inhibition of cell proliferation, which aligns with our previous research, although no reduction in cell proliferation was observed using a PAX2 small interfering RNA (siRNA). We downloaded publicly available RNA-sequencing data and clinical histories of RCC patients from The Cancer Genome Atlas (TCGA) database. Based on the expression levels of PAX2, PAX6, and PAX8, RCC patients were categorized into two PAX expression subtypes, PAXClusterA and PAXClusterB, exhibiting significant differences in clinical characteristics. We found that the PAXClusterA expression subgroup was associated with favorable clinical outcomes and better overall survival. These findings provide novel insights into the association between PAX gene expression levels and clinical outcomes in RCC patients, potentially contributing to improved treatment strategies for RCC.

Molecular Typing Based on Oxidative Stress Genes and Establishment of Prognostic Characteristics of 7 Genes in Lung Adenocarcinoma

Oxidative stress could maintain different biological processes in human cancer. However, the effect of oxidative stress on lung adenocarcinoma (LUAD) should be studied. This study analyzed the expression and clinical importance of oxidative stress in LUAD in detail. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were employed for obtaining LUAD expression profiles. Based on oxidative stress-related genes, molecular subtypes substantially correlated with the LUAD prognosis were discovered with ConsensusClusterPlus. Differentially expressed genes (DEGs) among subtypes were found using the Limma software package. Least absolute shrinkage and selection operator- (Lasso-) Cox analysis was employed to create the polygenic risk model. RiskScore and clinically relevant features were used to create nomograms. By utilizing oxidative stress-related genes and reliable clustering, stable molecular subtypes were first discovered. The prognosis, clinical characteristics, route characteristics, and immunological characteristics of these three molecular subtypes were all different. Subsequently, by using differential expression genes among molecular subtypes and Lasso, 7 main genes linked with the oxidative stress phenotype were discovered. A prognostic risk model was also built on the basis of major genes associated with the oxidative stress phenotype. The model demonstrated a high level of resilience and was unaffected by clinical-pathological features. It played a stable predictive role in independent datasets. Ultimately, to improve the prognosis model and survival prediction, RiskScore (RS) was combined with clinicopathological variables, and a decision tree model was used. The model exhibited a high prediction accuracy as well as the ability to predict survival. This research found that oxidative stress-related genes have a major involvement in the onset and progression of LUAD and that they may influence LUAD susceptibility to immunotherapy and standard chemotherapy. Furthermore, the identified risk models for 7 genes linked with oxidative stress exhibited could assist clinical treatment decisions and prognosis prediction. The classifier could be used as a molecular diagnostic tool for assessing LUAD patients' prognosis risk.

AFF3 is a novel prognostic biomarker and a potential target for immunotherapy in gastric cancer

Background

Gastric cancer (GC) is one of the most common cancers worldwide with a poor prognosis. The tumor microenvironment (TME) serves a pivotal role in affecting the prognosis and efficacy of immunotherapy. Given the poor prognosis of GC patients and the limitation of immunotherapy, we urged to identify new prognostic and immunotherapeutic biomarkers.

Methods

The transcriptome data were downloaded from the TCGA, GEO, and GEPIA databases, and performed differential analysis of AFF3 in tumor samples and normal samples. The UALCAN, Kaplan–Meier plotter and GEPIA databases were employed to assess the correlation of AFF3 with clinicopathological characteristics and prognosis. The potential mechanism of AFF3 was explored by the GO and KEGG enrichment. The potential role of AFF3 on tumor‐infiltrating immune cells (TIICs) was explored by TIMER2.0 and TISIDB. TIMER2.0 and SangerBox3.0 databases were, respectively, used to determine the correlation of AFF3 with immune checkpoint (ICs), tumor mutational burden (TMB), and microsatellite instability (MSI) in GC.

Results

We found significant downregulation of AFF3 in GC tissues as compared with normal tissues. However, GC patients having a higher expression of AFF3 were found to have worse clinicopathological characteristics and prognosis. Moreover, the GO enrichment analysis illustrated that AFF3 might regulate the immune cells in the TME. In addition, the AFF3 was positively correlated with TIICs, ICs, TMB, and MSI.

Conclusion

Here, we conclude that AFF3 may be a promising potential marker for the diagnosis and prognosis of GC patients, and may influence response to ICIs by affecting TIICs and ICs expression in the TME.

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.

Identification of Tumor Antigens and Immune Subtypes in Lung Adenocarcinoma for mRNA Vaccine Development

Cancer vaccines are emerging as a viable strategy for cancer treatment. In the current study, we screened for genes associated with the prognosis of patients with lung adenocarcinoma and positively correlated with antigen-presenting cell infiltration and identified KLRG1 and CBFA2T3 as potential tumor antigens for mRNA vaccines in lung adenocarcinoma (LUAD). Further analyses of immune subtypes revealed that patients with early-stage LUAD, high immune cell infiltration, high immune checkpoint expression, and low tumor mutation burden might benefit from mRNA vaccination. Moreover, we identified four biomarkers that can be used to assess mRNA vaccination suitability. We also identified potentially sensitive anti-cancer drugs for populations not suitable for vaccination by means of anti-cancer drug susceptibility prediction. Overall, we provided a new perspective for mRNA vaccine treatment strategies for LUAD and emphasized the importance of precise and personalized treatments.

Unraveling the Expression Patterns of Immune Checkpoints Identifies New Subtypes and Emerging Therapeutic Indicators in Lung Adenocarcinoma

Immune checkpoint genes (ICGs) play pivotal roles in tumor immune microenvironment (TIME), and thus, targeting them represents a promising strategy for cancer immunotherapy. However, the genetic landscape of ICGs in lung adenocarcinoma (LUAD) is still unknown. Herein, we comprehensively evaluated the ICG expression profiles of 1439 LUAD samples and linked ICG expression patterns with infiltration of immune cells, clinical features, and response to immune checkpoint blockade (ICB). The ICGscore was developed to quantify ICG expression patterns of individual patient by principal component analysis algorithms. Three distinct ICG expression patterns and three ICG-related genomic clusters were determined, which were implicated in different clinical outcomes, level of immune infiltrates, and biological process. LUAD patients were subdivided into high- and low-ICGscore subgroups. Patients with higher ICGscore were characterized by favorable survival outcomes, increased immune cell infiltration, and enhanced expression of ICGs. Further analysis revealed that lower ICGscore was associated with greater tumor mutation loads and higher mutation rates of TTN, KEAP1, and ZFHX4. High ICGscore has the potential to be a robust indicator in clinical benefit of immunotherapy. Taken together, unraveling the ICG expression patterns will advance our understanding of heterogeneity of TIME and guides more effective immunotherapeutic strategies in LUAD.