The DDR-related gene signature with cell cycle checkpoint function predicts prognosis, immune activity, and chemoradiotherapy response in lung adenocarcinoma

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. However, there is currently a lack of reliable signatures for identifying patients who would benefit from immunotherapy. We conducted a comparative analysis of two immunotherapy cohorts (OAK and POPLAR) and utilized single-factor COX regression to identify genes that significantly impact the prognosis of LUAD. Based on the TCGA-LUAD dataset, we employed a combination of 101 machine learning algorithms to construct a model and selected the optimal model. The model was validated on five GEO datasets and compared with 144 previously published signatures to assess its performance. Subsequently, we explored the underlying biological mechanisms through tumor mutation burden analysis, enrichment analysis, and immune infiltration analysis. An immunotherapy prognostic prediction signature (IPPS) was constructed based on 13 genes, showing robust performance in the TCGA-LUAD dataset. IPPS exhibited consistent predictive accuracy in the validation cohorts. Compared to 144 previously published signatures, IPPS consistently ranked among the top in terms of C-index values. Further exploration revealed differences between high and low-IPPS groups in terms of tumor mutation burden, pathway enrichment, and immune infiltration. IPPS demonstrates strong predictive capabilities for the prognosis of LUAD patients, offering the potential to identify suitable candidates for immunotherapy and contribute to precision treatment strategies for LUAD.

Involvement of ICAM5 in Carcinostasis Effects on LUAD Based on the ROS1-Related Prognostic Model

Background

Lung cancer is the most common type of cancer in the world. In lung adenocarcinoma (LUAD), studies on receptor tyrosine kinase ROS proto-oncogene 1 (ROS1) have mainly focused on the oncogenic effects of its fusion mutations, whereas ROS1 has been reported to be aberrantly expressed in a variety of cancers and can extensively regulate the growth, survival, and proliferation of tumor cells through multiple signaling pathways. The comprehensive analysis of ROS1 expression has not been fully investigated regarding its predictive value for LUAD patients.

Methods

Gene expression profiles collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to build and validate prognostic risk models. The association of ROS1 with overall survival and the immune landscape was obtained from the Tumor Immune Estimation Resource (TIMER) database. The following analyses were performed using the R package to determine the model's validity: pathway dysregulation analysis, gene set enrichment analysis, Gene Oncology analysis, immune invasion analysis, chemotherapy, radiotherapy, and immunotherapy sensitivity analysis. Finally, we conducted a pan-cancer analysis and performed in vitro experiments to explore the regulatory role of intercellular adhesion molecule 5 (ICAM5) in the progression of LUAD.

Results

We constructed a 17-gene model that categorized patients into two risk groups. The model had predictive accuracy for tumor prognosis and was specific for patients with high ROS1 expression. Comprehensive analysis showed that patients in the high-risk group were characterized by marked dysregulation of multiple pathways (eg, unfolded protein response), immune suppression of the tumor microenvironment, and poor benefit from immunotherapy and radiotherapy compared with patients in the low-risk group. PLX4720 may be a suitable treatment for the high-risk patient population. The ICAM5 gene has been demonstrated to inhibit the proliferation, cell cycle, invasion, and migration of LUAD cells.

Conclusion

We constructed a 17-gene prognostic risk model and found differences in immune-related cells, biological processes, and prognosis among patients in different risk groups based on the correlation between ROS1 and immunity. Personalized therapy may play an essential role in treatment. We further investigated the role of ICAM5 in inhibiting the malignant bioactivity of LUAD cells.

A novel fatty acid metabolism-related signature identifies MUC4 as a novel therapy target for esophageal squamous cell carcinoma

Fatty acid metabolism has been identified as an emerging hallmark of cancer, which was closely associated with cancer prognosis. Whether fatty acid metabolism-related genes (FMGs) signature play a more crucial role in biological behavior of esophageal squamous cell carcinoma (ESCC) prognosis remains unknown. Thus, we aimed to identify a reliable FMGs signature for assisting treatment decisions and prognosis evaluation of ESCC. In the present study, we conducted consensus clustering analysis on 259 publicly available ESCC samples. The clinical information was downloaded from The Cancer Genome Atlas (TCGA, 80 ESCC samples) and Gene Expression Omnibus (GEO) database (GSE53625, 179 ESCC samples). A consensus clustering arithmetic was used to determine the FMGs molecular subtypes, and survival outcomes and immune features were evaluated among the different subtypes. Kaplan-Meier analysis and the receiver operating characteristic (ROC) was applied to evaluate the reliability of the risk model in training cohort, validation cohort and all cohorts. A nomogram to predict patients' 1-year, 3-year and 5-year survival rate was also studied. Finally, CCK-8 assay, wound healing assay, and transwell assay were implemented to evaluate the inherent mechanisms of FMGs for tumorigenesis in ESCC. Two subtypes were identified by consensus clustering, of which cluster 2 is preferentially associated with poor prognosis, lower immune cell infiltration. A fatty acid (FA) metabolism-related risk model containing eight genes (FZD10, TACSTD2, MUC4, PDLIM1, PRSS12, BAALC, DNAJA2 and ALOX12B) was established. High-risk group patients displayed worse survival, higher stromal, immune and ESTIMATE scores than in the low-risk group. Moreover, a nomogram revealed good predictive ability of clinical outcomes in ESCC patients. The results of qRT-PCR analysis revealed that the MUC4 and BAALC had high expression level, and FZD10, PDLIM1, TACSTD2, ALOX12B had low expression level in ESCC cells. In vitro, silencing MUC4 remarkably inhibited ESCC cell proliferation, invasion and migration. Our study fills the gap of FMGs signature in predicting the prognosis of ESCC patients. These findings revealed that cluster subtypes and risk model of FMGs had effects on survival prediction, and were expected to be the potential promising targets for ESCC.

Prognostic significance and immune landscape of a cell cycle progression-related risk model in bladder cancer

BACKGROUND

A greater emphasis has been placed on the part of cell cycle progression (CCP) in cancer in recent years. Nevertheless, the precise connection between CCP-related genes and bladder cancer (BCa) has remained elusive. This study endeavors to establish and validate a reliable risk model incorporating CCP-related factors, aiming to predict both the prognosis and immune landscape of BCa.

METHODS

Clinical information and RNA sequencing data were collected from the GEO and TCGA databases. Univariate and multivariate Cox regression analyses were conducted to construct a risk model associated with CCP. The performance of the model was assessed using ROC and Kaplan-Meier survival analyses. Functional enrichment analysis was employed to investigate potential cellular functions and signaling pathways. The immune landscape was characterized using CIBERSORT algorithms. Integration of the risk model with various clinical variables led to the development of a nomogram.

RESULTS

To build the risk model, three CCP-related genes (RAD54B, KPNA2, and TPM1) were carefully chosen. ROC and Kaplan-Meier survival analysis confirm that our model has good performance. About immunological infiltration, the high-risk group showed decreased levels of regulatory T cells and dendritic cells coupled with increased levels of activated CD4 + memory T cells, M2 macrophages, and neutrophils. Furthermore, the nomogram showed impressive predictive power for OS at 1, 3, and 5 years.

CONCLUSION

This study provides new insights into the association between the CCP-related risk model and the prognosis of BCa, as well as its impact on the immune landscape.

PANoptosis-related long non-coding RNA signature to predict the prognosis and immune landscapes of pancreatic adenocarcinoma

Background

Cancer growth is significantly influenced by processes such as pyroptosis, apoptosis, and necroptosis that underlie PANoptosis, a proinflammatory programmed cell death. Several studies have examined the long non-coding RNAs (lncRNAs) associated with pancreatic adenocarcinoma (PAAD). However, the predictive value of lncRNAs related to PANoptosis for PAAD has not been established.

Methods

The Clinical Genome Atlas database was used to obtain the transcriptome 、clinical data and the corresponding mutation data of the patients with PAAD in this study. The least absolute shrinkage and selection operator regression analysis was employed to obtain prognosis-related lncRNAs for constructing a risk signature. According to the median risk score of the signature, patients with PAAD were grouped into low- and high-risk groups to further compare the survival prognosis of different risk groups. Time-dependent receiver operating characteristic curves, c-index analysis, nomograms, principal component analysis and univariate Cox and multivariate Cox regression were performed for the internal validation of the signature. In addition, enrichment analysis of different genes was performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Lastly, differences in tumor mutation burden (TMB), immune function, tumor immune dysfunction and rejection (TIDE), and drug response were determined for the two risk groups.

Results

The signature was constructed with six PANoptosis-related lncRNAs (AC067817.2、LINC02004、AC243829.1、AC092171.5、AP005233.2、AC004687.1) that predicted the prognosis of the patients with PAAD. Survival curves showed that patients in the two risk groups had statistically significant differences in prognosis (P < 0.05), and multi-cox regression analysis identified risk score as an independent risk factor for PAAD prognosis, and internal validation of nomograms showed high confidence in the signature. GO and KEGG enrichment analysis showed functional and pathway differences between the high- and low-risk groups. TMB evaluation demonstrated that patients in the high-risk group had a higher frequency of mutations. The TIDE score indicated that the high-risk group had a lower risk of immunotherapy escape and better immunotherapy outcomes. Additionally, the two risk groups revealed significantly different responses to 11 anticancer drugs.

Conclusion

We identified a novel risk signature for PANoptosis-related lncRNAs, which is a standalone prognostic indicator for PAAD. The PANoptosis-related lncRNA risk signature may be relevant for immunotherapy and a therapeutic target for PAAD.

Development and validation of a novel DNA damage repair-related long non-coding RNA signature in predicting prognosis, immunity, and drug sensitivity in uterine corpus endometrial carcinoma

Background

DNA damage response (DDR) confer resistance to chemoradiotherapy in cancer cells. However, the role of DDR-related lncRNAs (DRLs) in uterine corpus endometrial carcinoma (UCEC) is poorly understood. In this study, we aimed to identify a DRL-related prognostic signature that could guide the clinical treatment of UCEC.

Methods

We extracted transcriptome and clinical data of patients with UCEC from The Cancer Genome Atlas (TCGA) database and identified DRLs using Spearman correlation analysis. Univariate and multivariate Cox analyses were used to determine candidate prognostic DRLs. The samples were randomly divided into training and test cohorts in a 1:1 ratio. A DRL-related risk signature was constructed from the training cohort data using the least absolute shrinkage and selection operator (LASSO) algorithm, and validated using the test and entire cohorts. Subsequently, a prognostic nomogram was developed using a multivariate Cox regression analysis. The functional annotation, immune microenvironment, tumor mutation burden (TMB), immune checkpoint blockade (ICB) efficacy, and drug sensitivity were also comprehensively analyzed between different risk groups. Finally, the function of AC019069.1 was validated in vitro.

Results

A novel risk signature was developed based on nine DRLs. The risk score efficiently predicted the prognosis of patients with UCEC. Based on the median risk score, two subgroups were identified. The DDR-related pathways were upregulated in the high-risk group. Additionally, high-risk patients have low immune activity, poor response to ICB, and weak sensitivity to chemotherapeutic agents, possibly because of the proficient DDR system. Finally, we demonstrated AC019069.1 could promote cell proliferation, decrease apoptosis and maintain genome stability of UCEC cells.

Conclusions

The developed DRL-related signature can predict the prognosis, immune microenvironment, immunotherapy, and chemoradiotherapy responsiveness of UCEC. Our study also revealed the potential value of DDR-targeted therapy in treating high-risk patients with UCEC.

The DNA damage repair-related lncRNAs signature predicts the prognosis and immunotherapy response in gastric cancer

Background

Gastric cancer (GC) is one of the most prevalent cancers, and it has unsatisfactory overall treatment outcomes. DNA damage repair (DDR) is a complicated process for signal transduction that causes cancer. lncRNAs can influence the formation and incidence of cancers by influencing DDR-related mRNAs/miRNAs. A DDR-related lncRNA prognostic model is urgently needed to improve treatment strategies.

Methods

The data of GC samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. A total of 588 mRNAs involved in DDR were selected from MSigDB, 62 differentially expressed mRNAs from TCGA-STAD were obtained, and 137 lncRNAs were correlated with these mRNAs. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to develop a DDR-related lncRNA prognostic model. Based on the risk model, the differentially expressed gene signature A/B in the low-risk and high-risk groups of TCGA-STAD was identified for further validation.

Results

The prognosis model of 5 genes (AC145285.6, MAGI2-AS3, AL590705.3, AC007405.3, and LINC00106) was constructed and classified into two risk groups. We found that GC patients with a low-risk score had a better OS than those with a high-risk score. We found that the high-risk group tended to have higher TME scores. We also found that patients in the high-risk group had a higher proportion of resting CD4 T cells, monocytes, M2 macrophages, resting dendritic cells, and resting mast cells, whereas the low-risk subgroup had a greater abundance of activated CD4 T cells, follicular helper T cells, M0 macrophages, and M1 macrophages. We observed significant differences in the T-cell exclusion score, T-cell dysfunction, MSI, and TMB between the two risk groups. In addition, we found that patients treated with immunotherapy in the low-RS score group had a longer survival and a better prognosis than those in the high-RS score group.

Conclusion

The prognostic model has a significant role in the TME, clinicopathological characteristics, prognosis, MSI, and drug sensitivity. We also discovered that patients treated with immunotherapy in the low-RS score group had a better prognosis. This work provides a foundation for improving the prognosis and response to immunotherapy among patients with GC.

A novel DNA damage repair gene-related prognostic model for evaluating the prognosis and tumor microenvironment infiltration of esophageal squamous cell carcinoma

BACKGROUND

This study aimed to investigate the potential prognostic value of DNA damage repair genes (DDRGs) in esophageal squamous cell carcinoma (ESCC) and their relationship with immune-related characteristics.

METHODS

We analyzed DDRGs of the Gene Expression Omnibus database (GSE53625). Subsequently, the GSE53625 cohort was used to construct a prognostic model based on least absolute shrinkage and selection operator regression, and Cox regression analysis was used to construct a nomogram. The immunological analysis algorithms explored the differences between the potential mechanism, tumor immune activity, and immunosuppressive genes in the high- and low-risk groups. Of the prognosis model-related DDRGs, we selected PPP2R2A for further investigation. Functional experiments were conducted to evaluate the effect on ESCC cells in vitro.

RESULTS

A 5-DDRG (ERCC5, POLK, PPP2R2A, TNP1 and ZNF350) prediction signature was established for ESCC, stratifying patients into two risk groups. Multivariate Cox regression analysis showed that the 5-DDRG signature was an independent predictor of overall survival. Immune cells such as CD4 T cells and monocytes displayed lower infiltration levels in the high-risk group. Additionally, the immune, ESTIMATE, and stromal scores in the high-risk group were all considerably higher than those in the low-risk group. Functionally, knockdown of PPP2R2A significantly suppressed cell proliferation, migration and invasion in two ESCC cell lines (ECA109 and TE1).

CONCLUSION

The clustered subtypes and prognostic model of DDRGs could effectively predict the prognosis and immune activity of ESCC patients.

Identification of a Novel Gene Signature with DDR and EMT Difunctionalities for Predicting Prognosis, Immune Activity, and Drug Response in Breast Cancer

Breast cancer, with an overall poor clinical prognosis, is one of the most heterogeneous cancers. DNA damage repair (DDR) and epithelial-mesenchymal transition (EMT) have been identified to be associated with cancer's progression. Our study aimed to explore whether genes with both functions play a more crucial role in the prognosis, immune, and therapy response of breast cancer patients. Based on the Cancer Genome Atlas (TCGA) cancer database, we used LASSO regression analysis to identify the six prognostic-related genes with both DDR and EMT functions, including TP63, YWHAZ, BRCA1, CCND2, YWHAG, and HIPK2. Based on the six genes, we defined the risk scores of the patients and reasonably analyzed the overall survival rate between the patients with the different risk scores. We found that overall survival in higher-risk-score patients was lower than in lower-risk-score patients. Subsequently, further GO and KEGG analyses for patients revealed that the levels of immune infiltration varied for patients with high and low risk scores, and the high-risk-score patients had lower immune infiltration's levels and were insensitive to treatment with chemotherapeutic agents. Furthermore, the Gene Expression Omnibus (GEO) database validated our findings. Our data suggest that TP63, YWHAZ, BRCA1, CCND2, YWHAG, and HIPK2 can be potential genetic markers of prognostic assessment, immune infiltration and chemotherapeutic drug sensitivity in breast cancer patients.

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

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