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Abstract Lung cancer, specifically the histological subtype lung adenocarcinoma (LUAD), has the highest global occurrence and fatality rate. Extensive research has indicated that RNA alterations encompassing m6A, m5C, and m1A contribute actively to tumorigenesis, drug resistance, and immunotherapy responses in LUAD. Nevertheless, the absence of a dependable predictive model based on m6A/m5C/m1A-associated genes hinders accurately predicting the prognosis of patients diagnosed with LUAD. In this study, we collected patient data from The Cancer Genome Atlas (TCGA) and identified genes related to m6A/m5C/m1A modifications using the GeneCards database. The “ConsensusClusterPlus” R package was used to produce molecular subtypes by utilizing genes relevant to m6A/m5C/m1A identified through differential expression and univariate Cox analyses. An independent prognostic factor was identified by constructing a prognostic signature comprising six genes (SNHG12, PABPC1, IGF2BP1, FOXM1, CBFA2T3, and CASC8). Poor overall survival and elevated expression of human leukocyte antigens and immune checkpoints were correlated with higher risk scores. We examined the associations between the sets of genes regulated by m6A/m5C/m1A and the risk model, as well as the immune cell infiltration, using algorithms such as ESTIMATE, CIBERSORT, TIMER, ssGSEA, and exclusion (TIDE). Moreover, we compared tumor stemness indices (TSIs) by considering the molecular subtypes related to m6A/m5C/m1A and risk signatures. Analyses were performed based on the risk signature, including stratification, somatic mutation analysis, nomogram construction, chemotherapeutic response prediction, and small-molecule drug prediction. In summary, we developed a prognostic signature consisting of six genes that have the potential for prognostication in patients with LUAD and the design of personalized treatments that could provide new versions of personalized management for these patients.

Abstract Background Increasing evidence suggests that hepatocellular carcinoma (HCC) stem cells (LCSCs) play an essential part in HCC recurrence, metastasis, and chemotherapy and radiotherapy resistance. Multiple studies have demonstrated that stemness-related genes facilitate the progression of tumors. However, the mechanism by which stemness-related genes contribute to HCC is not well understood. Here, we aim to construct a stemness-related score (SRscores) model for deeper analysis of stemness-related genes, assisting with the prognosis and individualized treatment of HCC patients.Further, we found that the gene LPCAT1 was highly expressed in tumor tissues by immunohistochemistry, and sphere-forming assay revealed that knockdown of LPCAT1 inhibited the sphere-forming ability of hepatocellular carcinoma cells. Methods We used the TCGA-LIHC dataset to screen stemness-related genes of HCC from the MSigDB database. Prognosis, tumor microenvironment, immunological checkpoints, tumor immune dysfunction, rejection, treatment sensitivity, and putative biological pathways were examined. Random forest created the SRscores model. The anti-PD-1/anti-CTLA4 immunotherapy, tumor mutational burden, medication sensitivity, and cancer stem cell index were compared between the high- and low-risk score groups. We also examined risk scores for different cell types using single-cell RNA sequencing data and correlated transcription factor activity in cancer stem cells with SRscores genes. Finally, we tested core marker expression and biological functions. Results Patients can be divided into two subtypes (Cluster1 and Cluster2) based on the TCGA-LIHC dataset's identification of 11 stemness-related genes. Additionally, a SRscores was developed based on subtypes. Cluster2 and the group with the lowest SRscores had superior survival and immunotherapy response than Cluster1 and the group with the highest SRscores. The group with a high SRscores was significantly more enriched in classical tumor pathways than the group with a low SRscores. Multiple transcription factors and SRscores genes are correlated. The core gene LPCAT1 is highly expressed in rat liver cancer tissues and promotes tumor cell sphere formation. Conclusion A SRscores model can be utilized to predict the prognosis of HCC patients as well as their response to immunotherapy.

Abstract：: Background: One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC. Methods: In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The “ConsensusClusterPlus” program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors. Results: We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.

Background: Increasing evidence suggests that hepatocellular carcinoma (HCC) stem cells (LCSCs) play an essential part in HCC recurrence, metastasis, and chemotherapy and radiotherapy resistance. Multiple studies have demonstrated that stemness-related genes facilitate the progression of tumors. However, the mechanism by which stemness-related genes contribute to HCC is not well understood. Here, we aim to construct a stemness-related score (SRscores) model for deeper analysis of stemness-related genes, assisting with the prognosis and individualized treatment of HCC patients.Further, we found that the gene LPCAT1 was highly expressed in tumor tissues by immunohistochemistry, and sphere-forming assay revealed that knockdown of LPCAT1 inhibited the sphere-forming ability of hepatocellular carcinoma cells. Methods: We used the TCGA-LIHC dataset to screen stemness-related genes of HCC from the MSigDB database. Prognosis, tumor microenvironment, immunological checkpoints, tumor immune dysfunction, rejection, treatment sensitivity, and putative biological pathways were examined. Random forest created the SRscores model. The anti-PD-1/anti-CTLA4 immunotherapy, tumor mutational burden, medication sensitivity, and cancer stem cell index were compared between the high- and low-risk score groups. We also examined risk scores for different cell types using single-cell RNA sequencing data and correlated transcription factor activity in cancer stem cells with SRscores genes. Finally, we tested core marker expression and biological functions. Results: Patients can be divided into two subtypes (Cluster1 and Cluster2) based on the TCGA-LIHC dataset's identification of 11 stemness-related genes. Additionally, a SRscores was developed based on subtypes. Cluster2 and the group with the lowest SRscores had superior survival and immunotherapy response than Cluster1 and the group with the highest SRscores. The group with a high SRscores was significantly more enriched in classical tumor pathways than the group with a low SRscores. Multiple transcription factors and SRscores genes are correlated. The core gene LPCAT1 is highly expressed in rat liver cancer tissues and promotes tumor cell sphere formation. Conclusion: A SRscores model can be utilized to predict the prognosis of HCC patients as well as their response to immunotherapy. [ABSTRACT FROM AUTHOR]

Immunotherapy is a promising treatment for advanced colorectal cancers (CRCs). However, immunotherapy resistance remains a common problem. Immunogenic cell death (ICD), a form of regulated cell death, induces adaptive immunity, thereby enhancing anti-tumor immunity. Research increasingly suggests that inducing ICD is a promising avenue for cancer immunotherapy and identifying ICD-related biomarkers for CRCs would create a new direction for targeted therapies. Thus, this study used bioinformatics to address these questions and create a prognostic signature, aiming to improve individualized CRC treatment. We identified two ICD -related molecular subtypes of CRCs. The high subtype showed pronounced immune cell infiltration, high immune activity, and high expression of human leukocyte antigen and immune checkpoints genes. Subsequently, we constructed and validated a prognostic signature comprising six genes (CD1A, TSLP, CD36, TIMP1, MC1R, and NRG1) using random survival forest analyses. Further analysis using this prediction model indicated that patients with CRCs in the low-risk group exhibited favorable clinical outcomes and better immunotherapy responses than those in the high-risk group. Our findings provide novel insights into determining the prognosis and design of personalized immunotherapeutic strategies for patients with CRCs. [ABSTRACT FROM AUTHOR]

Cuprotosis is a novel mechanism of cell death that differs from known mechanisms, which depends on mitochondrial respiration and is closely related to lipoylated components of the tricarboxylic acid (TCA) cycle. However, it is unclear whether cuprotosis-related genes (CRGs) affect the tumor microenvironment (TME) and prognosis of patients with gastric cancer. In this study, the genetic and transcriptional characteristics of CRGs in gastric cancer (GC) were analyzed, and five CRGs that were differentially expressed and correlated with the survival of patients were obtained. Two different molecular subtypes were identified according to the five CRGs. Then, we constructed a CRG_score applied to patients of any age, gender, and stage. Subsequently, we found that cluster B and a high CRG_score had a worse prognosis, fewer immune checkpoints, and higher tumor immune dysfunction and exclusion (TIDE) compared to cluster A and a low CRG_score. In addition, two subtypes and the CRG_score were closely associated with clinicopathological characteristics, human leukocyte antigens (HLAs) and TME cell infiltration. A high CRG_score was featured with decreased microsatellite instability-high (MSI-H) and mutational burden. Meanwhile, the CRG_score was significantly related to the cancer stem cell (CSC) index and chemotherapeutic response. Moreover, we developed a nomogram to predict the survival probability of patients. Our study explained the role of CRGs in GC, and the prognostic signature could potentially provide an approach for personalized tumor therapy.

Background: N7-methylguanosine (m7G), one of the most common posttranscriptional modifications, can be present in tRNA, mRNA, and miRNA to mediate the progression of various tumors. However, the possible role of m7G in gastric cancer (GC) is still unknown. Materials and Methods: In this study, SNVs (single nucleotide variations), CNVs (copy number variations), and methylation of m7G-related genes (m7GRGs) were analyzed. The relationship between them and the expression of m7GRGs and prognosis of GC patients was explored. Based on 13 prognostic-related m7GRGs, 567 GC samples were classified into three subtypes using the ConsensusClusterPlus package. we compared survival status, clinical traits, immune cell infiltration, immune checkpoints, tumor microenvironment (TME), tumor immune dysfunction and exclusion (TIDE), and potential biological pathways among the three subtypes. Then, patients were again grouped into different genetic subtypes based on the DEGs among the three subtypes. In addition, a prognostic m7GRG_Score was constructed using five risk genes applicable to patients of any age, gender and stage. We also assessed tumor mutational burden (TMB), microsatellite instability (MSI), cancer stem cell (CSC) index, sensitivity of antineoplastic drugs, efficacy of anti-PD-1 and anti-CTLA4 immunotherapy between high and low m7GRG_Score groups. Finally, we established a nomogram based on m7GRG_Score and tumor stage to enhance the clinical application of the model. miRNAs and lncRNAs that could regulate expression of risk genes were searched. Results: SNVs, CNVs, and methylation of m7GRGs were associated with m7GRGs expression. However, they did not significantly affect the survival of GC patients. Our results also confirmed that patients in subtypes B and C and low m7GRG_Score groups had longer survival time, better clinical stage, more immune cell infiltration, fewer immune escape and dysfunction compared to subtype A and high m7GRG_Score groups. A low m7GRG_score was featured with increased microsatellite instability-high (MSI-H), TMB, and efficacy of immunotherapy. Conclusion: The m7GRG_Score model may become a beneficial tool for predicting prognosis and guiding personalized treatment in GC patients. These findings will improve our knowledge of m7G in GC and provide new methods for more effective treatment strategies. [ABSTRACT FROM AUTHOR]

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package "ConsensusClusterPlus" was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients. [ABSTRACT FROM AUTHOR]

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package “ConsensusClusterPlus” was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.

Background: One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC., Methods: In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The "ConsensusClusterPlus" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors., Results: We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Cuprotosis is a novel mechanism of cell death that differs from known mechanisms, which depends on mitochondrial respiration and is closely related to lipoylated components of the tricarboxylic acid (TCA) cycle. However, it is unclear whether cuprotosis-related genes (CRGs) affect the tumor microenvironment (TME) and prognosis of patients with gastric cancer. In this study, the genetic and transcriptional characteristics of CRGs in gastric cancer (GC) were analyzed, and five CRGs that were differentially expressed and correlated with the survival of patients were obtained. Two different molecular subtypes were identified according to the five CRGs. Then, we constructed a CRG_score applied to patients of any age, gender, and stage. Subsequently, we found that cluster B and a high CRG_score had a worse prognosis, fewer immune checkpoints, and higher tumor immune dysfunction and exclusion (TIDE) compared to cluster A and a low CRG_score. In addition, two subtypes and the CRG_score were closely associated with clinicopathological characteristics, human leukocyte antigens (HLAs) and TME cell infiltration. A high CRG_score was featured with decreased microsatellite instability-high (MSI-H) and mutational burden. Meanwhile, the CRG_score was significantly related to the cancer stem cell (CSC) index and chemotherapeutic response. Moreover, we developed a nomogram to predict the survival probability of patients. Our study explained the role of CRGs in GC, and the prognostic signature could potentially provide an approach for personalized tumor therapy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Dong, Zhao, Zhang and Wang.)

Background: N 7 -methylguanosine (m7G), one of the most common post-transcriptional modifications, can be present in tRNA, mRNA, and miRNA to mediate the progression of various tumors. However, the possible role of m7G in gastric cancer (GC) is still unknown., Materials and Methods: In this study, SNVs (single nucleotide variations), CNVs (copy number variations), and methylation of m7G-related genes (m7GRGs) were analyzed. The relationship between them and the expression of m7GRGs and prognosis of GC patients was explored. Based on 13 prognostic-related m7GRGs, 567 GC samples were classified into three subtypes using the ConsensusClusterPlus package. we compared survival status, clinical traits, immune cell infiltration, immune checkpoints, tumor microenvironment (TME), tumor immune dysfunction and exclusion (TIDE), and potential biological pathways among the three subtypes. Then, patients were again grouped into different genetic subtypes based on the DEGs among the three subtypes. In addition, a prognostic m7GRG_Score was constructed using five risk genes applicable to patients of any age, gender and stage. We also assessed tumor mutational burden (TMB), microsatellite instability (MSI), cancer stem cell (CSC) index, sensitivity of antineoplastic drugs, efficacy of anti-PD-1 and anti-CTLA4 immunotherapy between high and low m7GRG_Score groups. Finally, we established a nomogram based on m7GRG_Score and tumor stage to enhance the clinical application of the model. miRNAs and lncRNAs that could regulate expression of risk genes were searched., Results: SNVs, CNVs, and methylation of m7GRGs were associated with m7GRGs expression. However, they did not significantly affect the survival of GC patients. Our results also confirmed that patients in subtypes B and C and low m7GRG_Score groups had longer survival time, better clinical stage, more immune cell infiltration, fewer immune escape and dysfunction compared to subtype A and high m7GRG_Score groups. A low m7GRG_score was featured with increased microsatellite instability-high (MSI-H), TMB, and efficacy of immunotherapy., Conclusion: The m7GRG_Score model may become a beneficial tool for predicting prognosis and guiding personalized treatment in GC patients. These findings will improve our knowledge of m7G in GC and provide new methods for more effective treatment strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Dong, Chen, Wang, Hao, Zhang, Jiao, Zhao, Peng and Zhan.)