Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine

A novel prognostic signature related to programmed cell death in osteosarcoma

Background

Osteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.

Method

We retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.

Result

In our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.

Conclusion

The five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

Procyanidin C1 inhibits tumor growth and metastasis in colon cancer via modulating miR-501-3p/HIGD1A axis

INTRODUCTION

Although colon (COAD) and rectal adenocarcinoma (READ) combined to refer to colorectal cancer (CRC), substantial clinical evidence urged that CRC should be treated as two different cancers due to compared with READ, COAD showed higher morbidity and worse 5-year survival.

OBJECTIVES

This study has tried to screen for the crucial gene that caused the worse prognosis and investigate its mechanism for mediating tumor growth and metastases in COAD. Meanwhile, the potential anti-COAD compound implicated in this mechanism was identified and testified from 1,855 food-borne chemical kits. This study aims to bring a new perspective to the development of new anti-COAD drugs and personalized medicine for patients with COAD.

METHODS AND RESULTS

The survival-related hub genes in COAD and READ were screened out from The Cancer Genome Atlas (TCGA) database and the results showed that HIGD1A, lower expressed in COAD than in READ, was associated with poor prognosis in COAD patients, but not in READ. Over-expressed HIGD1A suppressed CRC cell proliferation, invasion, and migration in vitro and in vivo. Meanwhile, the different expressed microRNA profiles between COAD and READ showed that miR-501-3p was highly expressed in COAD and inhibited HIGD1A expression by targeting 3'UTR of HIGD1A. MiR-501-3p mimics promoted cell proliferation and metastasis in CRC cells. In addition, Procyanidin C1 (PCC1), a kind of natural polyphenol has been verified as a potential miR-501-3p inhibitor. In vitro and in vivo, PCC1 promoted HIGD1A expression by suppressing miR-501-3p and resulted in inhibited tumor growth and metastasis.

CONCLUSION

The present study verified that miR-501-3p/HIGD1A axis mediated tumor growth and metastasis in COAD. PCC1, a flavonoid that riched in food exerts anti-COAD effects by inhibiting miR-501-3p and results in the latter losing the ability to suppress HIGD1A expression. Subsequently, unfettered HIGD1A inhibited tumor growth and metastasis in COAD.

Pathomics Signature for Prognosis and Chemotherapy Benefits in Stage III Colon Cancer

Importance

The current TNM staging system may not provide adequate information for prognostic purposes and to assess the potential benefits of chemotherapy for patients with stage III colon cancer.

Objective

To develop and validate a pathomics signature to estimate prognosis and benefit from chemotherapy using hematoxylin-eosin (H-E)-stained slides.

Design, Setting, and Participants

This retrospective prognostic study used data from consecutive patients with histologically confirmed stage III colon cancer at 2 medical centers between January 2012 and December 2015. A total of 114 pathomics features were extracted from digital H-E-stained images from Nanfang Hospital of Southern Medical University, Guangzhou, China, and a pathomics signature was constructed using a least absolute shrinkage and selection operator Cox regression model in the training cohort. The associations of the pathomics signature with disease-free survival (DFS) and overall survival (OS) were evaluated. Patients at the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, formed the validation cohort. Data analysis was conducted from September 2022 to March 2023.

Main Outcomes and Measures

The prognostic accuracy of the pathomics signature as well as its association with chemotherapy response were evaluated.

Results

This study included 785 patients (mean [SD] age, 62.7 [11.1] years; 437 [55.7%] male). A pathomics signature was constructed based on 4 features. Multivariable analysis revealed that the pathomics signature was an independent factor associated with DFS (hazard ratio [HR], 2.46 [95% CI, 2.89-4.13]; P < .001) and OS (HR, 2.78 [95% CI, 2.34-3.31]; P < .001) in the training cohort. Incorporating the pathomics signature into pathomics nomograms resulted in better performance for the estimation of prognosis than the traditional model in a concordance index comparison in the training cohort (DFS: HR, 0.88 [95% CI, 0.86-0.89] vs HR, 0.73 [95% CI, 0.71-0.75]; P < .001; OS: HR, 0.85 [95% CI, 0.84-0.86] vs HR, 0.74 [95% CI, 0.72-0.76]; P < .001) and validation cohort (DFS: HR, 0.83 [95% CI, 0.82-0.85] vs HR, 0.70 [95% CI, 0.67-0.72]; P < .001; OS: HR, 0.80 [95% CI, 0.78-0.82] vs HR, 0.69 [0.67-0.72]; P < .001). Further analysis revealed that patients with a low pathomics signature were more likely to benefit from chemotherapy (eg, combined cohort: DFS: HR, 0.44 [95% CI, 0.28-0.69]; P = .001; OS: HR, 0.43 [95% CI, 0.29-0.64]; P < .001).

Conclusions and Relevance

These findings suggest that a pathomics signature could help identify patients most likely to benefit from chemotherapy in stage III colon cancer.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction.

METHOD

We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models.

RESULT

Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis.

CONCLUSION

The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

Innovative Surgical Approaches That Improve Individual Outcomes in Advanced Breast Cancer

Breast cancer is the most common cause of cancer death in women and the second cause in the general population. The incidence has increased over time. Women in developing countries often present at an advanced stage where initial surgery is not feasible. Short disease-free intervals, the number of metastatic organs and liver metastasis were consistently associated with poor overall survival. Surgery is an integral part of the therapeutic plan for locally advanced breast cancer. The integration of surgical care into the management of patients with advanced cancer has changed substantially with the use of neoadjuvant chemotherapy. Also, more recently, neoadjuvant endocrine therapy and targeted therapies offer new opportunities to downsize the tumor burden and transform the role of surgery for this population from palliation to largely curative intent. Innovative surgical approach to the primary tumor in metastatic disease may provide survival benefits and local control in some patients. Similar to systemic therapy, surgical therapy for secondary dissemination should be considered in certain cases for improved individual outcomes. Advances in reconstructive techniques have improved the quality of life of these patients.

RNA modification-related genes illuminate prognostic signature and mechanism in esophageal squamous cell carcinoma

Emerging studies have demonstrated the link between RNA modifications and various cancers, while the predictive value and functional mechanisms of RNA modification-related genes (RMGs) in esophageal squamous cell carcinoma (ESCC) remain unclear. Here we established a prognostic signature for ESCC based on five RMGs. The analysis of ESCC clinical samples further verified the prognostic power of the prognostic signature. Moreover, we found that the knockdown of NSUN6 promotes ESCC progression in vitro and in vivo, whereas the overexpression of NSUN6 inhibits the malignant phenotype of ESCC cells. Mechanically, NSUN6 mediated tRNA m5C modifications selectively enhance the translation efficiency of CDH1 mRNA in a codon dependent manner. Rescue assays revealed that E-cadherin is an essential downstream target that mediates NSUN6's function in the regulation of ESCC progression. These findings offer additional insights into the link between ESCC and RMGs, as well as provide potential strategies for ESCC management and therapy.

Clinical Outcomes of Patients with HER2 Positive Metastatic Breast Cancer to the Brain, with First-Line Trastuzumab, Pertuzumab and Chemotherapy, in a Real-World Setting

Background

In this observational study, we analyzed the treatment patterns and clinical outcomes of patients with human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (mBC) who developed brain metastases during their disease in a 2.7 million-member public health-provider in Israel.

Methods

Newly diagnosed patients with mBC who initiated first-line treatment between January 2013 and June 2021 were identified. Time on treatment (ToT) and overall survival (OS) were assessed at a minimum of 6 months follow-up (cutoff: December 2021).

Results

We identified a total of 61 patients: 98.4% females, median age 50 years (IQR = 44-63), 85% invasive ductal tumors, 44% hormone receptor positive, 51% performance status 0-1. The median duration of follow-up was 6.2 years. All patients initiated a combination treatment of trastuzumab, pertuzumab, and chemotherapy (TPC), and 72% moved to second-line treatment during the study follow-up period (82% ado-trastuzumab emtansine). The median ToT for first-line and second-line treatments were 16.9 months (95% CI = 13.9-27.7) and 7.9 months (95% CI = 5.6-10.9), respectively. The median overall survival (OS) was 45.5 months (95% CI = 35.4-71.2) from the initiation of first-line treatment. When considering the timing of brain metastases, the median OS was 36.3 months (95% CI = 10.0-NR) for those diagnosed upfront (n = 15, 25%), 59.1 months (95% CI = 32.5-NR) for those diagnosed while on TPC (n = 25, 41%), and 40.8 months (95% CI = 35.4-NR) for those diagnosed at a later stage (n = 21, 34%). The median OS from brain metastases diagnosis was 25.1 months (95% CI = 17.0-34.6).

Conclusion

Patients with upfront brain involvement at the time of mBC diagnosis had shorter survival compared to those who started TPC without brain metastases. Nonetheless, the overall results from this study compare favorably with previous studies and contribute to understanding the value of traditional treatment options, which will serve as a baseline for future treatment strategies in the real-world setting.

M7G-related tumor immunity: novel insights of RNA modification and potential therapeutic targets

RNA modifications play a pivotal role in regulating cellular biology by exerting influence over distribution features and molecular functions at the post-transcriptional level. Among these modifications, N7-methylguanosine (m7G) stands out as one of the most prevalent. Over recent years, significant attention has been directed towards understanding the implications of m7G modification. This modification is present in diverse RNA molecules, including transfer RNAs, messenger RNAs, ribosomal RNAs, and other noncoding RNAs. Its regulation occurs through a series of specific methyltransferases and m7G-binding proteins. Notably, m7G modification has been implicated in various diseases, prominently across multiple cancer types. Earlier studies have elucidated the significance of m7G modification in the context of immune biology regulation within the tumor microenvironment. This comprehensive review culminates in a synthesis of findings related to the modulation of immune cells infiltration, encompassing T cells, B cells, and various innate immune cells, all orchestrated by m7G modification. Furthermore, the interplay between m7G modification and its regulatory proteins can profoundly affect the efficacy of diverse adjuvant therapeutics, thereby potentially serving as a pivotal biomarker and therapeutic target for combinatory interventions in diverse cancer types.

Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.

m7G-related genes predict prognosis and affect the immune microenvironment and drug sensitivity in osteosarcoma

Background: Osteosarcoma (OS), a primary malignant bone tumor, confronts therapeutic challenges rooted in multidrug resistance. Comprehensive understanding of disease occurrence and progression is imperative for advancing treatment strategies. m7G modification, an emerging post-transcriptional modification implicated in various diseases, may provide new insights to explore OS pathogenesis and progression. Methods: The m7G-related molecular landscape in OS was probed using diverse bioinformatics analyses, encompassing LASSO Cox regression, immune infiltration assessment, and drug sensitivity analysis. Furthermore, the therapeutic potential of AZD2014 for OS was investigated through cell apoptosis and cycle assays. Eventually, multivariate Cox analysis and experimental validations, were conducted to investigate the independent prognostic m7G-related genes. Results: A comprehensive m7G-related risk model incorporating eight signatures was established, with corresponding risk scores correlated with immune infiltration and drug sensitivity. Drug sensitivity analysis spotlighted AZD2014 as a potential therapeutic candidate for OS. Subsequent experiments corroborated AZD2014's capability to induce G1-phase cell cycle arrest and apoptosis in OS cells. Ultimately, multivariate Cox regression analysis unveiled the independent prognostic importance of CYFIP1 and EIF4A1, differential expressions of which were validated at histological and cytological levels. Conclusion: This study furnishes a profound understanding of the contribution of m7G-related genes to the pathogenesis of OS. The discerned therapeutic potential of AZD2014, in conjunction with the identification of CYFIP1 and EIF4A1 as independent risk factors, opens novel vistas for the treatment of OS.

Analysis of m7G-Related signatures in the tumour immune microenvironment and identification of clinical prognostic regulators in breast cancer

BACKGROUND

Breast cancer is a malignant tumour that seriously threatens women's life and health and exhibits high inter-individual heterogeneity, emphasising the need for more in-depth research on its pathogenesis. While internal 7-methylguanosine (m7G) modifications affect RNA processing and function and are believed to be involved in human diseases, little is currently known about the role of m7G modification in breast cancer.

METHODS AND RESULTS

We elucidated the expression, copy number variation incidence and prognostic value of 24 m7G-related genes (m7GRGs) in breast cancer. Subsequently, based on the expression of these 24 m7GRGs, consensus clustering was used to divide tumour samples from the TCGA-BRCA dataset into four subtypes based on significant differences in their immune cell infiltration and stromal scores. Differentially expressed genes between subtypes were mainly enriched in immune-related pathways such as 'Ribosome', 'TNF signalling pathway' and 'Salmonella infection'. Support vector machines and multivariate Cox regression analysis were applied based on these 24 m7GRGs, and four m7GRGs-AGO2, EIF4E3, DPCS and EIF4E-were identified for constructing the prediction model. An ROC curve indicated that a nomogram model based on the risk model and clinical factors had strong ability to predict the prognosis of breast cancer. The prognoses of patients in the high- and low-TMB groups were significantly different (p = 0.03). Moreover, the four-gene signature was able to predict the response to chemotherapy.

CONCLUSIONS

In conclusion, we identified four different subtypes of breast cancer with significant differences in the immune microenvironment and pathways. We elucidated prognostic biomarkers associated with breast cancer and constructed a prognostic model involving four m7GRGs. In addition, we predicted the candidate drugs related to breast cancer based on the prognosis model.

Underexplored reciprocity between genome-wide methylation status and long non-coding RNA expression reflected in breast cancer research: potential impacts for the disease management in the framework of 3P medicine

Breast cancer (BC) is the most common female malignancy reaching a pandemic scale worldwide. A comprehensive interplay between genetic alterations and shifted epigenetic regions synergistically leads to disease development and progression into metastatic BC. DNA and histones methylations, as the most studied epigenetic modifications, represent frequent and early events in the process of carcinogenesis. To this end, long non-coding RNAs (lncRNAs) are recognized as potent epigenetic modulators in pathomechanisms of BC by contributing to the regulation of DNA, RNA, and histones' methylation. In turn, the methylation status of DNA, RNA, and histones can affect the level of lncRNAs expression demonstrating the reciprocity of mechanisms involved. Furthermore, lncRNAs might undergo methylation in response to actual medical conditions such as tumor development and treated malignancies. The reciprocity between genome-wide methylation status and long non-coding RNA expression levels in BC remains largely unexplored. Since the bio/medical research in the area is, per evidence, strongly fragmented, the relevance of this reciprocity for BC development and progression has not yet been systematically analyzed. Contextually, the article aims at:consolidating the accumulated knowledge on both-the genome-wide methylation status and corresponding lncRNA expression patterns in BC andhighlighting the potential benefits of this consolidated multi-professional approach for advanced BC management. Based on a big data analysis and machine learning for individualized data interpretation, the proposed approach demonstrates a great potential to promote predictive diagnostics and targeted prevention in the cost-effective primary healthcare (sub-optimal health conditions and protection against the health-to-disease transition) as well as advanced treatment algorithms tailored to the individualized patient profiles in secondary BC care (effective protection against metastatic disease). Clinically relevant examples are provided, including mitochondrial health control and epigenetic regulatory mechanisms involved.

Multi-omics identification of an immunogenic cell death-related signature for clear cell renal cell carcinoma in the context of 3P medicine and based on a 101-combination machine learning computational framework

Background

Clear cell renal cell carcinoma (ccRCC) is a prevalent urological malignancy associated with a high mortality rate. The lack of a reliable prognostic biomarker undermines the efficacy of its predictive, preventive, and personalized medicine (PPPM/3PM) approach. Immunogenic cell death (ICD) is a specific type of programmed cell death that is tightly associated with anti-cancer immunity. However, the role of ICD in ccRCC remains unclear.

Methods

Based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), and weighted gene co-expression network (WGCNA) analyses, ICD-related genes were screened at both the single-cell and bulk transcriptome levels. We developed a novel machine learning framework that incorporated 10 machine learning algorithms and their 101 combinations to construct a consensus immunogenic cell death-related signature (ICDRS). ICDRS was evaluated in the training, internal validation, and external validation sets. An ICDRS-integrated nomogram was constructed to provide a quantitative tool for predicting prognosis in clinical practice. Multi-omics analysis was performed, including genome, single-cell transcriptome, and bulk transcriptome, to gain a more comprehensive understanding of the prognosis signature. We evaluated the response of risk subgroups to immunotherapy and screened drugs that target specific risk subgroups for personalized medicine. Finally, the expression of ICD-related genes was validated by qRT-PCR.

Results

We identified 131 ICD-related genes at both the single-cell and bulk transcriptome levels, of which 39 were associated with overall survival (OS). A consensus ICDRS was constructed based on a 101-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. ICDRS can also be used to predict the occurrence, development, and metastasis of ccRCC. Multivariate analysis verified it as an independent prognostic factor for OS, progression-free survival (PFS), and disease-specific survival (DSS) of ccRCC. The ICDRS-integrated nomogram provided a quantitative tool in clinical practice. Moreover, we observed distinct biological functions, mutation landscapes, and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. Notably, the immunophenoscore (IPS) score showed a significant difference between risk subgroups, suggesting a better response to immunotherapy in the high-risk group. Potential drugs targeting specific risk subgroups were also identified.

Conclusion

Our study constructed an immunogenic cell death-related signature that can serve as a promising tool for prognosis prediction, targeted prevention, and personalized medicine in ccRCC. Incorporating ICD into the PPPM framework will provide a unique opportunity for clinical intelligence and new management approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00327-3.