Derivation and Validation of a Prognostic Model for Cancer Dependency Genes Based on CRISPR-Cas9 in Gastric Adenocarcinoma

Global and single-nucleotide resolution detection of 7-methylguanosine in RNA

RNA modifications, including N-7-methylguanosine (m7G), are pivotal in governing RNA stability and gene expression regulation. The accurate detection of internal m7G modifications is of paramount significance, given recent associations between altered m7G deposition and elevated expression of the methyltransferase METTL1 in various human cancers. The development of robust m7G detection techniques has posed a significant challenge in the field of epitranscriptomics. In this study, we introduce two methodologies for the global and accurate identification of m7G modifications in human RNA. We introduce borohydride reduction sequencing (Bo-Seq), which provides base resolution mapping of m7G modifications. Bo-Seq achieves exceptional performance through the optimization of RNA depurination and scission, involving the strategic use of high concentrations of NaBH4, neutral pH and the addition of 7-methylguanosine monophosphate (m7GMP) during the reducing reaction. Notably, compared to NaBH4-based methods, Bo-Seq enhances the m7G detection performance, and simplifies the detection process, eliminating the necessity for intricate chemical steps and reducing the protocol duration. In addition, we present an antibody-based approach, which enables the assessment of m7G relative levels across RNA molecules and biological samples, however it should be used with caution due to limitations associated with variations in antibody quality between batches. In summary, our novel approaches address the pressing need for reliable and accessible methods to detect RNA m7G methylation in human cells. These advancements hold the potential to catalyse future investigations in the critical field of epitranscriptomics, shedding light on the complex regulatory roles of m7G in gene expression and its implications in cancer biology.

N7-methylguanosine methylation of tRNAs regulates survival to stress in cancer

Tumour progression and therapy tolerance are highly regulated and complex processes largely dependent on the plasticity of cancer cells and their capacity to respond to stress. The higher plasticity of cancer cells highlights the need for identifying targetable molecular pathways that challenge cancer cell survival. Here, we show that N7-guanosine methylation (m7G) of tRNAs, mediated by METTL1, regulates survival to stress conditions in cancer cells. Mechanistically, we find that m7G in tRNAs protects them from stress-induced cleavage and processing into 5' tRNA fragments. Our analyses reveal that the loss of tRNA m7G methylation activates stress response pathways, sensitising cancer cells to stress. Furthermore, we find that the loss of METTL1 reduces tumour growth and increases cytotoxic stress in vivo. Our study uncovers the role of m7G methylation of tRNAs in stress responses and highlights the potential of targeting METTL1 to sensitise cancer cells to chemotherapy.

METTL1 promotes tumorigenesis through tRNA-derived fragment biogenesis in prostate cancer

Newly growing evidence highlights the essential role that epitranscriptomic marks play in the development of many cancers; however, little is known about the role and implications of altered epitranscriptome deposition in prostate cancer. Here, we show that the transfer RNA N7-methylguanosine (m7G) transferase METTL1 is highly expressed in primary and advanced prostate tumours. Mechanistically, we find that METTL1 depletion causes the loss of m7G tRNA methylation and promotes the biogenesis of a novel class of small non-coding RNAs derived from 5'tRNA fragments. 5'tRNA-derived small RNAs steer translation control to favour the synthesis of key regulators of tumour growth suppression, interferon pathway, and immune effectors. Knockdown of Mettl1 in prostate cancer preclinical models increases intratumoural infiltration of pro-inflammatory immune cells and enhances responses to immunotherapy. Collectively, our findings reveal a therapeutically actionable role of METTL1-directed m7G tRNA methylation in cancer cell translation control and tumour biology.

SLC25A1-associated prognostic signature predicts poor survival in acute myeloid leukemia patients

Background: Acute myeloid leukemia (AML) is a heterogeneous malignant disease. SLC25A1, the gene encoding mitochondrial carrier subfamily of solute carrier proteins, was reported to be overexpressed in certain solid tumors. However, its expression and value as prognostic marker has not been assessed in AML. Methods: We retrieved RNA profile and corresponding clinical data of AML patients from the Beat AML, TCGA, and TARGET databases (TARGET_AML). Patients in the TCGA cohort were well-grouped into two group based on SLC25A1 and differentially expressed genes were determined between the SLC25A1 high and low group. The expression of SLC25A1 was validated with clinical samples. The survival and apoptosis of two AML cell lines were analyzed with SLC25A1 inhibitor (CTPI-2) treatment. Cox and the least absolute shrinkage and selection operator (LASSO) regression analyses were applied to Beat AML database to identify SLC25A1-associated genes for the construction of a prognostic risk-scoring model. Survival analysis was performed by Kaplan-Meier and receiver operator characteristic curves. Results: Our analysis revealed that high expressed level of SLC25A1 in AML patients correlates with unfavorable prognosis. Moreover, SLC25A1 expression was positively associated with metabolism activity. We further demonstrated that the inhibition of SLC25A1 could inhibit the proliferation and increase the apoptosis of AML cells. In addition, a panel of SLC25A1-associated genes, was identified to construct a prognostic risk-scoring model. This SLC25A1-associated prognostic signature (SPS) is an independent risk factor with high area under curve (AUC) values of receiver operating characteristic (ROC) curves. A high SPS in leukemia patients is associated with poor survival. A Prognostic nomogram including the SPS and other clinical parameters, was constructed and its predictive efficiency was confirmed. Conclusion: We have successfully established a SPS prognostic model that predict outcome and risk stratification in AML. This risk model can be used as an independent biomarker to assess prognosis of AML.

A molecular classification of gastric cancer associated with distinct clinical outcomes and validated by an XGBoost-based prediction model

Gastric cancer (GC) is a heterogeneous disease and a leading cause of cancer-related deaths. Discovering robust, clinically relevant molecular classifications is critical for guiding personalized therapies for GC. Here, we propose a refined molecular classification scheme for GC using integrated optimal algorithms and multi-omics data. Based on the important features of mRNA, microRNA, and DNA methylation data selected by the multivariate Cox regression model, three subtypes linked to distinct clinical outcomes were identified by combining similarity network fusion and consensus clustering methods. Three subtypes were validated by an extreme gradient boosting machine learning prediction model with 125 differentially expressed genes in multiple independent cohorts. The molecular characteristics of mutation signatures, characteristic gene sets, driver genes, and chemotherapy sensitivity for each subtype were also identified: subtype 1 was associated with favorable prognosis and characterized by high ARID1A and PIK3CA mutations, subtype 2 was associated with a poor prognosis and harbored high recurrent TP53 mutations, and subtype 3 was associated with high CHD1, APOA1 mutations, and a poor prognosis. The proposed three-subtype scheme achieved a better clinical prediction performance (area under the curve value = 0.71) than The Cancer Genome Atlas classification, which may provide a practical subtyping framework to improve the treatment of GC.

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images

Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based model (MultiDeepCox-SC) that predicts overall survival in patients with stomach cancer by integrating histopathological images, clinical data, and gene expression data. The MultiDeepCox-SC not only automatedly selects patches with more information for survival prediction, without manual labeling for histopathological images, but also identifies genetic and clinical risk factors associated with survival in stomach cancer. The prognostic accuracy of the MultiDeepCox-SC (C-index = 0.744) surpasses the result only based on histopathological image (C-index = 0.660). The risk score of our model was still an independent predictor of survival outcome after adjustment for potential confounders, including pathologic stage, grade, age, race, and gender on The Cancer Genome Atlas dataset (hazard ratio 1.555, p = 3.53e-08) and the external test set (hazard ratio 2.912, p = 9.42e-4). Our fully automated online prognostic tool based on histopathological images, clinical data, and gene expression data could be utilized to improve pathologists' efficiency and accuracy (https://yu.life.sjtu.edu.cn/DeepCoxSC).

Novel roles of METTL1/WDR4 in tumor via m7G methylation

As one of the prevalent posttranscriptional modifications of RNA, N⁷-methylguanosine (m⁷G) plays essential roles in RNA processing, metabolism, and function, mainly regulated by the methyltransferase-like 1 (METTL1) and WD repeat domain 4 (WDR4) complex. Emerging evidence suggests that the METTL1/WDR4 complex promoted or inhibited the processes of many tumors, including head and neck, lung, liver, colon, bladder cancer, and teratoma, dependent on close m⁷G methylation modification of tRNA or microRNA (miRNA). Therefore, METTL1 and m⁷G modification can be used as biomarkers or potential intervention targets, providing new possibilities for early diagnosis and treatment of tumors. This review will mainly focus on the mechanisms of METTL1/WDR4 via m⁷G in tumorigenesis and the corresponding detection methods.

CFDP1 promotes hepatocellular carcinoma progression through activating NEDD4/PTEN/PI3K/AKT signaling pathway

BACKGROUND AND AIMS

It is being increasingly reported that the Cranio Facial Development Protein 1 (CFDP1) plays a significant role in the onset and progression of tumors. Nonetheless, the underlying mechanisms associated with CFDP1 that contribute to hepatocellular carcinoma (HCC) and the specific biological role of CFDP1 remain vague.

METHODS

The Gene Expression Omnibus (GEO) database was analyzed to obtain the gene expression profiles as well as the matching clinical data of HCC patients. The gene co-expression network was developed by means of weighted gene co-expression network analysis (WGCNA) to screen for possible biomarkers that could be used for the purpose of predicting prognosis. The Cancer Genome Atlas (TCGA) and Gene Expression Profile Interaction Analysis (GEPIA) databases were used to assess the relationship between survival and expression. In addition, we identified the underlying mechanism associated with CFDP1 by analyzing the KEGG pathway database, applying the GSEA and GeneCards analysis method. We performed a sequence of experiments (in vivo and in vitro) for the purpose of investigating the specific function of CFDP1 in liver cancer.

RESULTS

The obtained results revealed high expression of CFDP1 in HCC tissues and cell lines. A positive correlation between the overexpression of CFDP1 and the adverse clinicopathological features was observed. Moreover, we observed that the low recurrence-free survival and overall survival were associated with CFDP1 overexpression. In addition, GeneCards and GSEA analysis showed that CFDP1 may interact with NEDD4 and participate in PTEN regulation. Meanwhile, CFDP1 can promote the malignant development of liver cancer in vivo and in vitro. The western blotting technique was also employed so as to examine the samples, and the findings demonstrated that CFDP1 enhanced the malignancy of HCC via the NEDD4-mediated PTEN/PI3K/AKT pathway.

CONCLUSION

We highlighted that CFDP1 played an oncogenic role in HCC and was identified as a possible clinical prognostic factor and a potential novel therapeutic target for HCC.

The potential role of N7-methylguanosine (m7G) in cancer

N⁷-methylguanosine (m7G), one of the most prevalent RNA modifications, has recently attracted significant attention. The m7G modification actively participates in biological and pathological functions by affecting the metabolism of various RNA molecules, including messenger RNA, ribosomal RNA, microRNA, and transfer RNA. Increasing evidence indicates a critical role for m7G in human disease development, especially cancer, and aberrant m7G levels are closely associated with tumorigenesis and progression via regulation of the expression of multiple oncogenes and tumor suppressor genes. Currently, the underlying molecular mechanisms of m7G modification in cancer are not comprehensively understood. Here, we review the current knowledge regarding the potential function of m7G modifications in cancer and discuss future m7G-related diagnostic and therapeutic strategies.

Glycosyltransferases in Cancer: Prognostic Biomarkers of Survival in Patient Cohorts and Impact on Malignancy in Experimental Models

Background: Glycosylation changes are a main feature of cancer. Some carbohydrate epitopes and expression levels of glycosyltransferases have been used or proposed as prognostic markers, while many experimental works have investigated the role of glycosyltransferases in malignancy. Using the transcriptomic data of the 21 TCGA cohorts, we correlated the expression level of 114 glycosyltransferases with the overall survival of patients. Methods: Using the Oncolnc website, we determined the Kaplan−Meier survival curves for the patients falling in the 15% upper or lower percentile of mRNA expression of each glycosyltransferase. Results: Seventeen glycosyltransferases involved in initial steps of N- or O-glycosylation and of glycolipid biosynthesis, in chain extension and sialylation were unequivocally associated with bad prognosis in a majority of cohorts. Four glycosyltransferases were associated with good prognosis. Other glycosyltransferases displayed an extremely high predictive value in only one or a few cohorts. The top were GALNT3, ALG6 and B3GNT7, which displayed a p < 1 × 10−9 in the low-grade glioma (LGG) cohort. Comparison with published experimental data points to ALG3, GALNT2, B4GALNT1, POFUT1, B4GALT5, B3GNT5 and ST3GAL2 as the most consistently malignancy-associated enzymes. Conclusions: We identified several cancer-associated glycosyltransferases as potential prognostic markers and therapeutic targets.

Proteomics and phosphoproteomics analysis of tissues for the reoccurrence prediction of colorectal cancer

BACKGROUND

Many stage II/III colorectal cancer (CRC) patients may relapse after routine treatments. Aberrant phosphorylation can regulate pathophysiological processes of tumors, and finding characteristic protein phosphorylation is an efficient approach for the prediction of CRC relapse.

RESEARCH DESIGN AND METHODS

We compared the tissue proteome and phosphoproteome of stage II/III CRC patients between the relapsed group (n = 5) and the non-relapsed group (n = 5). Phosphopeptides were enriched with Ti⁴⁺-IMAC material. We utilized label-free quantification-based proteomics to screen differentially expressed proteins and phosphopeptides between the two groups. Gene Ontology (GO) analysis and Ingenuity Pathway Analysis (IPA) were used for bioinformatics analysis.

RESULTS

The immune response of the relapsed group (Z-score -2.229) was relatively poorer than that of the non-relapsed group (Z-score 1.982), while viability of tumor was more activated (Z-score 2.895) in the relapsed group, which might cause increased relapse risk. The phosphorylation degrees of three phosphosites (phosphosite 1362 of TP53BP1, phosphosite 809 of VCL and phosphosite 438 of STK10) might be reliable prognostic biomarkers.

CONCLUSIONS

Some promising proteins and phosphopeptides were discovered to predict the relapse risk in postoperative follow-ups.

Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning

Background

Ovarian cancer (OC) is a malignancy exhibiting high mortality in female tumors. Glycosylation is a posttranslational modification of proteins but research has failed to demonstrate a systematic link between glycosylation-related signatures and tumor environment of OC.

Purpose

This study is aimed at developing a novel model with glycosylation-related messenger RNAs (GRmRNAs) to predict the prognosis and immune function in OC patients.

Methods

The transcriptional profiles and clinical phenotypes of OC patients were collected from the Gene Expression Omnibus and The Cancer Genome Atlas databases. A weighted gene coexpression network analysis and machine learning were performed to find the optimal survival-related GRmRNAs. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression were carried out to calculate the coefficients of each GRmRNA and compute the risk score of each patient as well as develop a prognostic model. A nomogram model was constructed, and several algorithms were used to investigate the relationship between risk subtypes and immune-infiltrating levels.

Results

A total of four signatures (ALG8, DCTN4, DCTN6, and UBB) were determined to calculate the risk scores, classifying patients into the high-and low-risk groups. High-risk patients exhibited significantly poorer survival outcomes, and the established nomogram model had a promising prediction for OC patients' prognosis. Tumor purity and tumor mutation burden were negatively correlated with risk scores. In addition, risk scores held statistical associations with pathway signatures such as Wnt, Hippo, and reactive oxygen species, and nonsynonymous mutation counts.

Conclusion

The currently established risk scores based on GRmRNAs can accurately predict the prognosis, the immune microenvironment, and the immunotherapeutic efficacy of OC patients.

Expression and Clinical Significance of Origin Recognition Complex Subunit 6 in Breast Cancer – A Comprehensive Bioinformatics Analysis

Objective

We aimed to investigate the expression, diagnostic and prognostic values, and potential molecular mechanisms of the origin recognition complex (ORC) in breast cancer (BC).

Methods

Kaplan–Meier estimation was used to assess the prognostic value of ORC genes, and Oncomine, TCGA, GEO and ULCAN databases were used to analyze their expression in BC. Wilcoxon rank-sum tests were used to evaluate the relationship between ORC gene expression levels and BC clinicopathological features. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of ORC genes in BC. Survival analysis was performed using Kaplan–Meier estimation and Cox regression. A nomogram was constructed to predict 1-, 3-, and 5-year survival probabilities in BC. Gene set enrichment analysis (GSEA) and immune infiltration were used to investigate potential molecular mechanisms of the ORC.

Results

ORC1L and ORC6L were highly expressed in BC compared with healthy tissue, while ORC5L expression patterns were inconsistent; no significant differences in ORC2L, ORC3L or ORC4L expression were observed between BC and healthy tissues. ORC1L and ORC6L expression levels were significantly correlated with age, tumor (T) stage and molecular subtype; ORC5L expression was significantly correlated with age and number of nearby lymph nodes with cancer (N stage). ORC6L expression had the highest diagnostic value in BC and was an independent prognostic factor for poor overall survival (OS). ORC6L may be involved in cell cycle progression and may regulate cancer signaling pathways, including NF-κB, P53, and WNT, in BC. ORC6L expression was also associated with immune infiltration.

Conclusion

ORC1L and ORC6L are highly expressed in BC; ORC6L has a high diagnostic value and is an independent prognostic factor for poor OS. ORC6L may be involved in the initiation and progression of BC by regulating cell cycle progression, promoting cancer signaling pathway activation, and influencing tumor immune cell infiltration.

Robust Glycogene-Based Prognostic Signature for Proficient Mismatch Repair Colorectal Adenocarcinoma

Background

Proficient mismatch repair (pMMR) colorectal adenocarcinoma (CRAC) metastasizes to a greater extent than MMR-deficient CRAC. Prognostic biomarkers are preferred in clinical practice. However, traditional biomarkers screened directly from sequencing are often not robust and thus cannot be confidently utilized.

Methods

To circumvent the drawbacks of blind screening, we established a new strategy to identify prognostic biomarkers in the conserved and specific oncogenic pathway and its regulatory RNA network. We performed RNA sequencing (RNA-seq) for messenger RNA (mRNA) and noncoding RNA in six pMMR CRAC patients and constructed a glycosylation-related RNA regulatory network. Biomarkers were selected based on the network and their correlation with the clinicopathologic information and were validated in multiple centers (n = 775).

Results

We constructed a competing endogenous RNA (ceRNA) regulatory network using RNA-seq. Genes associated with glycosylation pathways were embedded within this scale-free network. Moreover, we further developed and validated a seven-glycogene prognosis signature, GlycoSig (B3GNT6, GALNT3, GALNT8, ALG8, STT3B, SRD5A3, and ALG6) that prognosticate poor-prognostic subtype for pMMR CRAC patients. This biomarker set was validated in multicenter datasets, demonstrating its robustness and wide applicability. We constructed a simple-to-use nomogram that integrated the risk score of GlycoSig and clinicopathological features of pMMR CRAC patients.

Conclusions

The seven-glycogene signature served as a novel and robust prognostic biomarker set for pMMR CRAC, highlighting the role of a dysregulated glycosylation network in poor prognosis.

Comprehensive Characterization of RNA Processing Factors in Gastric Cancer Identifies a Prognostic Signature for Predicting Clinical Outcomes and Therapeutic Responses

RNA processing converts primary transcript RNA into mature RNA. Altered RNA processing drives tumor initiation and maintenance, and may generate novel therapeutic opportunities. However, the role of RNA processing factors in gastric cancer (GC) has not been clearly elucidated. This study presents a comprehensive analysis exploring the clinical, molecular, immune, and drug response features underlying the RNA processing factors in GC. This study included 1079 GC cases from The Cancer Genome Atlas (TCGA, training set), our hospital cohort, and two other external validation sets (GSE15459, GSE62254). We developed an RNA processing-related prognostic signature using Cox regression with the least absolute shrinkage and selection operator (LASSO) penalty. The prognostic value of the signature was evaluated using a multiple-method approach. The genetic variants, pathway activation, immune heterogeneity, drug response, and splicing features associated with the risk signature were explored using bioinformatics methods. Among the tested 819 RNA processing genes, we identified five distinct RNA processing patterns with specific clinical outcomes and biological features. A 10-gene RNA processing-related prognostic signature, involving ZBTB7A, METTL2B, CACTIN, TRUB2, POLDIP3, TSEN54, SUGP1, RBMS1, TGFB1, and PWP2, was further identified. The signature was a powerful and robust prognosis factor in both the training and validation datasets. Notably, it could stratify the survival of patients with GC in specific tumor-node-metastasis (TNM) classification subgroups. We constructed a composite prognostic nomogram to facilitate clinical practice by integrating this signature with other clinical variables (TNM stage, age). Patients with low-risk scores were characterized with good clinical outcomes, proliferation, and metabolism hallmarks. Conversely, poor clinical outcome, invasion, and metastasis hallmarks were enriched in the high-risk group. The RNA processing signature was also involved in tumor microenvironment reprogramming and regulating alternative splicing, causing different drug response features between the two risk groups. The low-risk subgroup was characterized by high genomic instability, high alternative splicing and might benefit from the immunotherapy. Our findings highlight the prognostic value of RNA processing factors for patients with GC and provide insights into the specific clinical and molecular features underlying the RNA processing-related signature, which may be important for patient management and targeting treatment.