Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population

Genomic Differences and Distinct TP53 Mutation Site-Linked Chemosensitivity in Early- and Late-Onset Gastric Cancer

BACKGROUND

Gastric cancer (GC) in younger patients often exhibits aggressive behavior and a poorer prognosis than that in older patients. Although the clinical differences may stem from oncogenic gene variations, it is unclear whether genetic differences exist between these groups. This study compared the genetic profiles of early- and late-onset GC and evaluated their impact on treatment outcomes.

METHODS

We analyzed genetic data from 1284 patients with GC in the Japanese nationwide Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database, comparing early-onset (≤ 39 years; n = 143) and late-onset (≥ 65 years; n = 1141) groups. The influence of TP53 mutations on the time to treatment failure (TTF) with platinum-based chemotherapy and the sensitivity of cancer cells with different TP53 mutation sites to oxaliplatin were assessed in vitro.

RESULTS

Early- and late-onset GC showed distinct genetic profiles, with fewer neoantigen-associated genetic changes observed in early-onset cases. In particular, TP53 has distinct mutation sites; R175H and R273 mutations are more frequent in early- and late-onset GC, respectively. The R175H mutation showed higher sensitivity to oxaliplatin in vitro, consistent with the longer TTF in early-onset patients (17.3 vs. 7.0 months, p = 0.013) when focusing on the patients with TP53 mutations.

CONCLUSION

Genomic differences, particularly in TP53 mutation sites, between early- and late-onset GC support the need for age-specific treatment strategies.

Therapeutic Targets for Gastric Cancer: Mendelian Randomization and Colocalization Analysis

BACKGROUND

Gastric cancer (GC) is one of the most prevalent malignancies in the world. Most patients are diagnosed at advanced stages of the disease, primarily attributable to the insidious nature of early symptoms and the infrequent occurrence of routine screening. Further biomarkers are still needed for more comprehensive analysis, targeted prognostication, and effective treatment strategies. Plasma proteins are promising biomarkers and potential drug targets in GC. This study aims to identify potential therapeutic targets for GC by conducting a comprehensive proteome-wide Mendelian randomization (MR) and colocalization analyses.

METHODS

Plasma proteins were obtained from the UK Biobank Pharma Proteomics Project (UKB-PPP), including Genome-Wide Association Study(GWAS)data of 1463 plasma proteins. Genetic associations with cancer were derived from the European Bioinformatics Institute (EBI) database, including 1029 patients and 475,087 controls (dataset: ebi-a-gcst90018849). MR analysis was conducted to assess the association between plasma proteins and the risk of developing cancer. Additionally, colocalization analysis was employed to investigate whether the identified proteins and gastric cancer exhibited shared incidental variants. Finally, using the extensive Finnish database in the R9 version, the potential harmful effects of target proteins on the treatment of gastric cancer were explored through the whole phenomenon association study (PheWAS).

RESULT

The results showed that 15 proteins may be associated with the risk of gastric cancer, and one protein is expected to become a therapeutic target for gastric cancer. There was a positive genetic association between plasma levels of 11 proteins and increased GC risk, while 4 proteins exhibited an inverse association with GC risk (P < 0.05). Colocalization analysis revealed that PPCDC and GC exhibited shared genetic loci among the 15 proteins examined, indicating that PPCDC may serve as potential direct target for intervention in GC. Further phenotype wide association studies showed that PPCDC (P < 0.05) could be associated with certain potential side effects.

CONCLUSION

Our research examined the causal relationship between plasma proteins and gastric cancer, shedding light on potential therapeutic targets. These findings have significant implications for the development of early diagnostic markers and targeted therapies for GC, potentially improving patient outcomes and survival rates. Future studies should validate these findings in diverse populations and explore the clinical applications of these targets.

Prognostic and immunological implications of paraptosis-related genes in lung adenocarcinoma: Comprehensive analysis and functional verification of hub gene

BACKGROUND

Lung adenocarcinoma (LUAD) poses significant clinical challenges due to its inherent heterogeneity and variable response to treatment. Recent research has specifically focused on elucidating the role of Paraptosis-related genes (PRGs) in the progression of cancer and the prognosis of patients.

METHODS

We conducted a comprehensive analysis of the differential expression of PRGs in LUAD. Additionally, univariate Cox regression analysis was utilized to determine the prognostic significance of these genes. Furthermore, consensus clustering was employed to differentiate molecular subtypes within LUAD, while immune heterogeneity was assessed. To evaluate treatment outcomes, the expression of immune checkpoint inhibitors was examined, and the sensitivity of LUAD patients to chemotherapy drugs was assessed. Moreover, machine learning algorithms were employed to construct a Paraptosis-related risk score with prognostic and immunological indicators. Finally, to validate the findings, in vitro experiments were performed to verify the regulatory effect of key PRGs on Paraptosis.

RESULTS

Our analysis identified 24 PRGs that exhibited differential expression, with CDKN3, TP53, and PHB emerging as the most prominently upregulated genes in tumor tissues. Among these genes, seven were identified as prognostic markers, with HSPB8 being the sole protective factor. Notably, our analysis also revealed the existence of two distinct molecular subtypes within LUAD, each characterized by unique prognoses and immune responses. Specifically, Subtype B displayed a poorer prognosis but demonstrated increased sensitivity to both chemotherapy and immunotherapy. In addition, our development of a Paraptosis-Associated Risk Score yielded a significant prognostic value in predicting patient outcomes. Furthermore, we found regulatory effect of CDKN3 on Paraptosis in two cell lines.

CONCLUSIONS

Our study highlights the importance of PRGs in LUAD, particularly in prognosis and treatment response. The identified molecular subtypes and Paraptosis-Associated Risk Score offer valuable insights for personalized treatment strategies.

Identifying high-risk candidates for prolonging progression-free survival in primary gastric carcinoma subject to "double invasion": an analytical approach utilizing lasso-cox regression

OBJECTIVE

To identify high-risk gastric carcinoma patients with concurrent vascular and neural invasion ("double invasion") who are at heightened risk of progression-free survival (PFS) decline, enabling personalized clinical management.

METHODS

In this multi-center retrospective study, 559 patients with double invasion who underwent curative gastrectomy between May 2002 and December 2020 were analyzed. Prognostic factors for PFS were identified using Lasso-Cox regression. Model validation included internal bootstrapping, calibration plots, and comparison against the American Joint Committee on Cancer(AJCC) 8th edition TNM staging system via Harrell's C-index, decision curve analysis (DCA), and time-dependent receiver operating characteristic (ROC) curves.

RESULTS

The nomogram integrated gender, positive lymph node count, surgical gastrectomy method, PTEN/FHIT expression levels, and maximum tumor diameter. It demonstrated superior predictive accuracy to AJCC staging, with a C-index of 0.651 (95% CI: 0.612-0.691) versus 0.543 (95% CI: 0.517-0.569). Calibration plots showed strong agreement between predicted and observed outcomes. The area under the curve(AUC) for 3- and 5-year PFS predictions were 0.719 (95% CI: 0.655-0.771) and 0.767 (95% CI: 0.670-0.841), respectively. DCA confirmed clinical utility across decision thresholds, and risk stratification effectively differentiated low- and high-risk groups. In the training cohort, the model significantly outperformed AJCC staging (NRI: 0.218, p < 0.01; IDI: 0.085, p < 0.01). However, this superiority was not statistically significant in the validation cohort (NRI: 0.141, p = 0.08; IDI: 0.031, p = 0.239).

CONCLUSION

We developed a Lasso-Cox regression-based nomogram to stratify PFS risk in gastric carcinoma patients with double invasion. While the model outperformed AJCC staging in training, validation cohort results highlight the need for further refinement. This tool holds potential for guiding tailored therapeutic strategies, though broader validation is warranted to confirm clinical applicability.

Eg5 and Diseases: From the Well-Known Role in Cancer to the Less-Known Activity in Noncancerous Pathological Conditions

Eg5 is a protein encoded by KIF11 gene and is primarily involved in correct mitotic cell division. It is also involved in nonmitotic processes such as polypeptide synthesis, protein transport, and angiogenesis. The scientific literature sheds light on the ubiquitous functions of KIF11 and its involvement in the onset and progression of different pathologies. This review focuses attention on two main points: (1) the correlation between Eg5 and cancer and (2) the involvement of Eg5 in noncancerous conditions. Regarding the first point, several tumors revealed an overexpression of this kinesin, thus pushing to look for new Eg5 inhibitors for clinical practice. In addition, the evaluation of Eg5 expression represents a crucial step, as its overexpression could predict a poor prognosis for cancer patients. Referring to the second point, in specific pathological conditions, the reduced activity of Eg5 can be one of the causes of pathological onset. This is the case of Alzheimer's disease (AD), in which Aβ and Tau work as Eg5 inhibitors, or in acquired immune deficiency syndrome (AIDS), in which Tat-mediated Eg5 determines the loss of CD4+ T-lymphocytes. Reduced Eg5 activity, due to mutations of KIF11 gene, is also responsible for pathological conditions such as microcephaly with or without chorioretinopathy, lymphedema, or intellectual disability (MCLRI) and familial exudative vitreous retinopathy (FEVR). In conclusion, this review highlights the double impact that overexpression or loss of function of Eg5 could have in the onset and progression of different pathological situations. This emphasizes, on one hand, a possible role of Eg5 as a potential biomarker and new target in cancer and, on the other hand, the promotion of Eg5 expression/activity as a new therapeutic strategy in different noncancerous diseases.

A performance evaluation of drug response prediction models for individual drugs

Drug response prediction is important to establish personalized medicine for cancer therapy. Model construction for predicting drug response (i.e., cell viability half-maximal inhibitory concentration [IC₅₀]) of an individual drug by inputting pharmacogenomics in disease models remains critical. Machine learning (ML) has been predominantly applied for prediction, despite the advent of deep learning (DL). Moreover, whether DL or traditional ML models are superior for predicting cell viability IC_50s has to be established. Herein, we constructed ML and DL drug response prediction models for 24 individual drugs and compared the performance of the models by employing gene expression and mutation profiles of cancer cell lines as input. We observed no significant difference in drug response prediction performance between DL and ML models for 24 drugs [root mean squared error (RMSE) ranging from 0.284 to 3.563 for DL and from 0.274 to 2.697 for ML; R² ranging from -7.405 to 0.331 for DL and from -8.113 to 0.470 for ML]. Among the 24 individual drugs, the ridge model of panobinostat exhibited the best performance (R² 0.470 and RMSE 0.623). Thus, we selected the ridge model of panobinostat for further application of explainable artificial intelligence (XAI). Using XAI, we further identified important genomic features for panobinostat response prediction in the ridge model, suggesting the genomic features of 22 genes. Based on our findings, results for an individual drug employing both DL and ML models were comparable. Our study confirms the applicability of drug response prediction models for individual drugs.

A nomogram based on cuproptosis-related genes predicts 7-year relapse-free survival in patients with estrogen receptor-positive early breast cancer

Introduction

Excess copper induces cell death by binding to lipoylated components of the tricarboxylic acid cycle. Although a few studies have examined the relationship between cuproptosis-related genes (CRGs) and breast cancer prognosis, reports on estrogen receptor-positive (ER+) breast cancer are lacking. Herein, we aimed to analyze the relationship between CRGs and outcomes in patients with ER+ early breast cancer (EBC).

Methods

We conducted a case-control study among patients with ER+ EBC presenting poor and favorable invasive disease-free survival (iDFS) at West China Hospital. Logistic regression analysis was performed to establish the association between CRG expression and iDFS. A cohort study was performed using pooled data from three publicly available microarray datasets in the Gene Expression Omnibus database. Subsequently, we constructed a CRG score model and a nomogram to predict relapse-free survival (RFS). Finally, the prediction performance of the two models was verified using training and validation sets.

Results

In this case-control study, high expression of LIAS, LIPT1, and ATP7B and low CDKN2A expression were associated with favorable iDFS. In the cohort study, high expression of FDX1, LIAS, LIPT1, DLD, PDHB, and ATP7B and low CDKN2A expression were associated with favorable RFS. Using LASSO-Cox analysis, a CRG score was developed using the seven identified CRGs. Patients in the low CRG score group had a reduced risk of relapse in both training and validation sets. The nomogram included the CRG score, lymph node status, and age. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram was significantly higher than the AUC of the CRG score at 7 years.

Conclusions

The CRG score, combined with other clinical features, could afford a practical long-term outcome predictor in patients with ER+ EBC.

A pretreatment transcriptomic signature that predicts outcomes of immunotherapy in melanoma

Identifying indicators of immunotherapy response are key to clinical treatment decisions. To date, immunotherapy is most widely used in melanoma because of its higher tumor mutation burden compared to other cancer types. However, less than half of melanoma patients can benefit from immune checkpoint inhibitor (ICI) therapy. For this reason, we deciphered pretreatment transcriptomes across a cohort of melanoma patients receiving anti-PD-1 or CTLA-4 alone (sICI) or in combination (cICI). We developed a two-gene signature that could predict the curative effect of ICI in melanoma by using the LASSO method. The pre-ICI signature displayed an equally competitive predictive power as the post-ICI irRECIST assessment that could offer clues regarding long-term ICI therapy response and facilitate risk stratification and treatment strategies.