A prospective prognostic signature for pancreatic adenocarcinoma based on ubiquitination-related mRNA-lncRNA with experimental validation in vitro and vivo

Comprehensive characterization of β-alanine metabolism-related genes in HCC identified a novel prognostic signature related to clinical outcomes

Hepatocellular carcinoma (HCC) stands out as the most prevalent type of liver cancer and a significant contributor to cancer-related fatalities globally. Metabolic reprogramming, particularly in glucose, lipid, and amino acid metabolism, plays a crucial role in HCC progression. However, the functions of β-alanine metabolism-related genes (βAMRGs) in HCC remain understudied. Therefore, a comprehensive evaluation of βAMRGs is required, specifically in HCC. Initially, we explored the pan-cancer landscape of βAMRGs, integrating expression profiles, prognostic values, mutations, and methylation levels. Subsequently, scRNA sequencing results indicated that hepatocytes had the highest scores of β-alanine metabolism. In the process of hepatocyte carcinogenesis, metabolic pathways were further activated. Using βAMRGs scores and expression profiles, we classified HCC patients into three subtypes and examined their prognosis and immune microenvironments. Cluster 3, characterized by the highest βAMRGs scores, displayed the best prognosis, reinforcing β-alanine's significant contribution to HCC pathophysiology. Notably, immune microenvironment, metabolism, and cell death modes significantly varied among the β-alanine subtypes. We developed and validated a novel prognostic panel based on βAMRGs and constructed a nomogram incorporating risk degree and clinicopathological characteristics. Among the model genes, EHHADH has been identified as a protective protein in HCC. Its expression was notably downregulated in tumors and exhibited a close correlation with factors such as tumor staging, grading, and prognosis. Immunohistochemical experiments, conducted using HCC tissue microarrays, substantiated the validation of its expression levels. In conclusion, this study uncovers β-alanine's significant role in HCC for the first time, suggesting new research targets and directions for diagnosis and treatment.

Association of the Advanced Lung Cancer Inflammation Index (ALI) and Gustave Roussy Immune (GRIm) score with immune checkpoint inhibitor efficacy in patients with gastrointestinal and lung cancer

OBJECTIVE

This study aimed to conduct a comprehensive analysis, evaluating the prognostic significance of the baseline Advanced Lung Cancer Inflammation Index (ALI) and Gustave Roussy Immune (GRIm) Score in patients undergoing immune checkpoint inhibitor (ICI) therapy.

METHODS

A comprehensive search was performed across various databases, including PubMed, the Cochrane Library, EMBASE, and Google Scholar, until October 21, 2023, to compile relevant articles for analysis. The investigation encompassed diverse clinical outcomes, including overall survival (OS) and progression-free survival (PFS).

RESULTS

This analysis included a total of 15 articles, comprising 19 studies involving 3335 patients. Among the 19 studies, nine studies focused on NSCLC, and six studies were conducted on HCC. Pooled results revealed that patients with elevated ALI levels experienced prolonged OS (HR: 0.51, 95% CI: 0.37-0.70, p < 0.001) and extended PFS (HR: 0.61, 95% CI: 0.52-0.72, p < 0.001). Furthermore, a GRIm score > 1 was associated with reduced OS (HR: 2.07, 95% CI: 1.47-2.92, p < 0.001) and diminished PFS (HR: 1.78, 95% CI: 1.35-2.34, p < 0.001) in cancer patients receiving ICIs. Subgroup analysis indicated that ALI cutoff values of 18 exhibited enhanced predictive potential. Additionally, for HCC patients, those with HCC-GRIm score > 2 showed a substantially decreased risk of mortality compared to individuals with HCC-GRIm score ≤ 2 (HR: 2.63, 95% CI: 1.89-3.65, p < 0.001).

CONCLUSION

The ALI and GRIm score served as dependable prognostic indicators for patients undergoing ICI therapy in the context of cancer treatment.

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients

Background

Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined.

Methods

We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD.

Results

Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion.

Conclusion

Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

Molecular characteristics and prognostic significances of lysosomal-dependent cell death in kidney renal clear cell carcinoma

Lysosomal-dependent cell death (LDCD) has an excellent therapeutic effect on apoptosis-resistant and drug-resistant tumors; however, the important role of LDCD-related genes (LDCD-RGs) in kidney renal clear cell carcinoma (KIRC) has not been reported. Initially, single-cell atlas of LDCD signal in KIRC was comprehensively depicted. We also emphasized the molecular characteristics of LDCD-RGs in various human neoplasms. Predicated upon the expressive quotients of LDCD-RGs, we stratified KIRC patients into tripartite cohorts denoted as C1, C2, and C3. Those allocated to the ambit of C1 evinced the most sanguine prognosis within the KIRC cohort, underscored by the acme of LDCD-RGs scores. This further confirms the significant role that LDCD-RGs play in both the pathophysiological foundation and clinical implications of KIRC. In culmination, by virtue of employing the LASSO-Cox analytical modality, we have ushered in an innovative and avant-garde prognostic framework tailored for KIRC, predicated on the bedrock of LDCD-RGs. The assemblage of KIRC instances was arbitrarily apportioned into constituents inclusive of a didactic cohort, an internally wielded validation cadre, and an externally administered validation cohort. Concurrently, patients were dichotomized into strata connoting elevated jeopardy synonymous with adverse prognostic trajectories, and conversely, diminished risk tantamount to favorable prognoses, contingent on the calibrated expressions of LDCD-RGs. Succinctly, our investigative findings serve to underscore the cardinal capacity harbored by LDCD-RGs within the KIRC milieu, concurrently birthing a pioneering prognostic schema intrinsically linked to the trajectory of KIRC and its attendant prognoses.

Integration of genomics and transcriptomics highlights the crucial role of chromosome 5 open reading frame 34 in various human malignancies

Although some data suggest that chromosome 5 open reading frame 34 (C5orf34) plays a pivotal part in the onset and disease progression of various cancers, there is no pan-cancer investigation of C5orf34 at present. This study sought to establish the predictive importance of C5orf34 in a variety of human malignancies and to understand its fundamental immunological function. In our research, we applied a combination of several bioinformatics techniques and basic experiments to investigate the differential expression of C5orf34, and its relationship with prognosis, methylation, single nucleotide variant, clinical characteristics, microsatellite instability, tumor mutational burden, copy number variation, and immune cell infiltration of several cancers from the database that is publicly available with the aim of identifying the potential prognostic markers. In this study we found that C5orf34 expression differed significantly among cancers types, according to the findings. The expression level of C5orf34 is markedly increased in the majority of malignancies when compared to normal tissues, which is significantly correlated with an unfavorable prognosis of patients. Immunohistochemical staining confirmed the findings that C5orf34 expression was remarkably up-regulated in a variety of gynecologic cancers. Moreover, C5orf34 expression was shown to be correlated with the clinical features of patients. C5orf34 was also found to be expressed with genes that code for the major immune suppressors, chemokines, immune activators, chemokine receptors, and histocompatibility complex. Finally, our study shows that C5orf34 has the potential to be employed as a prognostic biomarker. Moreover, it might regulate the immune microenvironment in a variety of malignancies.

Low geriatric nutritional risk index as a poor prognostic biomarker for immune checkpoint inhibitor treatment in solid cancer

Objective

In this investigation, we focused on the geriatric nutritional risk index (GNRI), a comprehensive metric that takes into account the patient's ideal weight, actual weight, and serum albumin levels to measure malnutrition. Our primary objective was to examine the predictive value of GNRI-defined malnutrition in determining the response to immunotherapy among cancer patients.

Methods

Relevant articles for this study were systematically searched in PubMed, the Cochrane Library, EMBASE, and Google Scholar up to July 2023. Our analysis evaluated overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR) as clinical outcomes.

Results

This analysis comprised a total of eleven articles encompassing 1,417 patients. The pooled results revealed that cancer patients with low GNRI levels exhibited shorter OS (HR: 2.64, 95% CI: 2.08-3.36, p < 0.001) and PFS (HR: 1.87, 95% CI: 1.46-2.41, p < 0.001), and lower ORR (OR: 0.46, 95% CI: 0.33-0.65, p < 0.001) and DCR (OR: 0.42, 95% CI: 0.29-0.61, p < 0.001). Sensitivity analyses confirmed that the above results were stable. Egger's and Begg's tests revealed that there was no publication bias in the above results.

Conclusion

Our results imply that the GNRI is a useful predictor of immunotherapy response in cancer patients.

Machine learning-enhanced insights into sphingolipid-based prognostication: revealing the immunological landscape and predictive proficiency for immunomotherapy and chemotherapy responses in pancreatic carcinoma

Background: With a poor prognosis for affected individuals, pancreatic adenocarcinoma (PAAD) is known as a complicated and diverse illness. Immunocytes have become essential elements in the development of PAAD. Notably, sphingolipid metabolism has a dual function in the development of tumors and the invasion of the immune system. Despite these implications, research on the predictive ability of sphingolipid variables for PAAD prognosis is strikingly lacking, and it is yet unclear how they can affect PAAD immunotherapy and targeted pharmacotherapy. Methods: The investigation process included SPG detection while also being pertinent to the prognosis for PAAD. Both the analytical capability of CIBERSORT and the prognostic capability of the pRRophetic R package were used to evaluate the immunological environments of the various HCC subtypes. In addition, CCK-8 experiments on PAAD cell lines were carried out to confirm the accuracy of drug sensitivity estimates. The results of these trials, which also evaluated cell survival and migratory patterns, confirmed the usefulness of sphingolipid-associated genes (SPGs). Results: As a result of this thorough investigation, 32 SPGs were identified, each of which had a measurable influence on the dynamics of overall survival. This collection of genes served as the conceptual framework for the development of a prognostic model, which was carefully assembled from 10 chosen genes. It should be noted that this grouping of patients into cohorts with high and low risk was a sign of different immune profiles and therapy responses. The increased abundance of SPGs was identified as a possible sign of inadequate responses to immune-based treatment approaches. The careful CCK-8 testing carried out on PAAD cell lines was of the highest importance for providing clear confirmation of drug sensitivity estimates. Conclusion: The significance of Sphingolipid metabolism in the complex web of PAAD development is brought home by this study. The novel risk model, built on the complexity of sphingolipid-associated genes, advances our understanding of PAAD and offers doctors a powerful tool for developing personalised treatment plans that are specifically suited to the unique characteristics of each patient.

The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy

BACKGROUND

Research on immunogenic cell death (ICD) in lung adenocarcinoma (LUAD) has been relatively limited. This study aims to create ICD-related signatures for accurate survival prognosis prediction in LUAD patients, addressing the challenge of lacking reliable early prognostic indicators for this type of cancer.

METHODS

Using single-cell RNA sequencing (scRNA-seq) analysis, ICD activity in cells was calculated by AUCell algorithm, divided into high- and low-ICD groups according to median values, and key ICD regulatory genes were identified through differential analysis, and these genes were integrated into TCGA data to construct prognostic signatures using LASSO and COX regression analysis, and multi-dimensional analysis of ICD-related signatures in terms of prognosis, immunotherapy, tumor microenvironment (TME), and mutational landscape.

RESULTS

The constructed signature reveals a pronounced disparity in prognosis between the high- and low-risk groups of LUAD patients. The statistical discrepancies in survival times among LUAD patients from both the TCGA and GEO databases further corroborate this observation. Additionally, heightened levels of immune cell infiltration expression are evidenced in the low-risk group, suggesting a potential benefit from immunotherapeutic interventions for these patients. The expression levels of pivotal risk-associated genes in tissue samples were assessed utilizing qRT-PCR, thereby unveiling PITX3 as a plausible therapeutic target in the context of LUAD.

CONCLUSIONS

Our constructed ICD-related signatures provide help in predicting the prognosis and immunotherapy of LUAD patients, and to some extent guide the clinical treatment of LUAD patients.

Deciphering Treg cell roles in esophageal squamous cell carcinoma: a comprehensive prognostic and immunotherapeutic analysis

Background: Esophageal squamous cell carcinoma (ESCC) is a prevalent and aggressive form of cancer that poses significant challenges in terms of prognosis and treatment. Regulatory T cells (Treg cells) have gained attention due to their influential role in immune modulation within the tumor microenvironment (TME). Understanding the intricate interactions between Treg cells and the tumor microenvironment is essential for unraveling the mechanisms underlying ESCC progression and for developing effective prognostic models and immunotherapeutic strategies. Methods: A combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq analysis was utilized to explore the role of Treg cells within the TME of ESCC. The accuracy and applicability of the prognostic model were assessed through multi-dimensional evaluations, encompassing an examination of the model's performance across various dimensions, such as the mutation landscape, clinical relevance, enrichment analysis, and potential implications for immunotherapy strategies. Results: The pivotal role of the macrophage migration inhibitory factor (MIF) signaling pathway within the ESCC TME was investigated, with a focus on its impact on Treg cells and other subpopulations. Through comprehensive integration of bulk sequencing data, a Treg-associated signature (TAS) was constructed, revealing that ESCC patients with elevated TAS (referred to as high-TAS individuals) experienced significantly improved prognoses. Heightened immune infiltration and increased expression of immune checkpoint markers were observed in high-TAS specimens. The model's validity was established through the IMvigor210 dataset, demonstrating its robustness in predicting prognosis and responsiveness to immunotherapy. Heightened therapeutic benefits were observed in immune-based interventions for high-TAS ESCC patients. Noteworthy differences in pathway enrichment patterns emerged between high and low-TAS cohorts, highlighting potential avenues for therapeutic exploration. Furthermore, the clinical relevance of key model genes was substantiated by analyzing clinical samples from ten paired tumor and adjacent tissues, revealing differential expression levels. Conclusion: The study established a TAS that enables accurate prediction of patient prognosis and responsiveness to immunotherapy. This achievement holds significant implications for the clinical management of ESCC, offering valuable insights for informed therapeutic interventions.

Role of exosome-mediated molecules SNORD91A and SLC40A1 in M2 macrophage polarization and prognosis of ESCC

BACKGROUND

Exosome-mediated interaction serves as a significant regulatory factor for M2 macrophage polarization in cancer.

METHODS

All accessible data were acquired from The Cancer Genome Atlas (TCGA) database and analyzed using R software. Molecules implicated in exocrine secretion were amassed from the ExoCarta database. Our research initially quantified the immune microenvironment in Esophageal Squamous Cell Carcinoma (ESCC) patients based on the expression profile sourced from the TCGA database. Additionally, we delved into the biological role of M2 macrophages in ESCC via Gene Set Enrichment Analysis (GSEA).

RESULTS

We observed that patients with high M2 macrophage infiltration typically have a poorer prognosis. Subsequently, a total of 1457 molecules were identified, with 103 of these molecules believed to function through exocrine mechanisms, as supported by data from the ExoCarta database. SNORD91A and SLC40A1 were ultimately pinpointed due to their correlation with patient prognosis. Moreover, we investigated their potential roles in ESCC, including biological enrichment, immune infiltration, and genomic instability analysis.

CONCLUSIONS

Our study identified exosome-associated molecules, namely SNORD91A and SLC40A1, which notably impact ESCC prognosis and local M2 macrophage recruitment, thereby presenting potential therapeutic targets for ESCC.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.