A new strategy to identify ADAM12 and PDGFRB as a novel prognostic biomarker for matrine regulates gastric cancer via high throughput chip mining and computational verification

BACKGROUND

Gastric cancer is a life-threatening disease that poses a serious risk to human health. Although there are numerous molecular targets for gastric cancer in clinical practice, they often exhibit low specificity and sensitivity. Consequently, this can result in a low early diagnosis rate, delayed treatment, and poor prognosis for patients with gastric cancer. Hence, it remains crucial to identify more precise diagnostic markers for this disease.

METHODS

This study utilized ceRNA chips and bioinformatics methods to investigate the key genes and mechanisms involved in matrine intervention in gastric cancer cells.

RESULTS

ADAM12 and PDGFRB are the key genes that are down-regulated after matrine intervention in gastric cancer cells. By conducting bioinformatics analysis, two ceRNA regulatory axes were identified, which are associated with the prognosis of gastric cancer. These axes are lncRNA DGCR5/hsa-miR-206/ADAM12 and circRNA ITGA3/hsa-miR-24-3p/PDGFRB.

CONCLUSION

The low expression of ADAM12 may weaken the digestion of extracellular matrix (ECM) molecules, which can result in the invasion and metastasis of tumor cells. This occurs without the catalysis of ECM proteases, thereby impacting the invasion and metastasis of gastric cancer cells. Additionally, the analysis of immune infiltration suggests that ADAM12 and PDGFRB may influence changes in the tumor immune microenvironment, thereby affecting the occurrence and development of gastric cancer. This study contributes to a deeper understanding of the role of the matrine-related ceRNA network in gastric cancer, providing a reference for clinical diagnosis and treatment. It holds significant importance in discovering new drug treatment targets.

Identification of a disulfidptosis-related genes signature for prognostic implication in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer. Additionally, disulfidptosis, a newly discovered type of cell death, has been found to be closely associated with the onset and progression of tumors.

METHODS

The study first identified genes related to disulfidptosis through correlation analysis. These genes were then screened using univariate cox regression and LASSO regression, and a prognostic model was constructed through multivariate cox regression. A nomogram was also created to predict the prognosis of LUAD. The model was validated in three independent data sets: GSE72094, GSE31210, and GSE37745. Next, patients were grouped based on their median risk score, and differentially expressed genes between the two groups were analyzed. Enrichment analysis, immune infiltration analysis, and drug sensitivity evaluation were also conducted.

RESULTS

In this study, we examined 21 genes related to disulfidptosis and developed a gene signature that was found to be associated with a poorer prognosis in LUAD. Our model was validated using three independent datasets and showed AUC values greater than 0.5 at 1, 3, and 5 years. Enrichment analysis revealed that the disulfidptosis-related genes signature had a multifaceted impact on LUAD, particularly in relation to tumor development, proliferation, and metastasis. Patients in the high-risk group exhibited higher tumor purity and lower stromal score, ESTIMATE score, and Immune score.

CONCLUSION

This study constructed a gene signature related to disulfidptosis in lung adenocarcinoma and analyzed its impact on the disease and its association with the tumor microenvironment. The findings of this research provide valuable insights into the understanding of lung adenocarcinoma and could potentially lead to the development of new treatment strategies.

Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma

Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One of the main goals in the related bioinformatics analyses is to provide stable genetic signatures able to include genes whose expression levels can be effective to predict the prognosis of the patients. In this study, we collected the prognostic signatures for neuroblastoma published in the biomedical literature, and noticed that the most frequent genes present among them were three: AHCY, DPYLS3, and NME1. We therefore investigated the prognostic power of these three genes by performing a survival analysis and a binary classification on multiple gene expression datasets of different groups of patients diagnosed with neuroblastoma. Finally, we discussed the main studies in the literature associating these three genes with neuroblastoma. Our results, in each of these three steps of validation, confirm the prognostic capability of AHCY, DPYLS3, and NME1, and highlight their key role in neuroblastoma prognosis. Our results can have an impact on neuroblastoma genetics research: biologists and medical researchers can pay more attention to the regulation and expression of these three genes in patients having neuroblastoma, and therefore can develop better cures and treatments which can save patients' lives.

Distinct Profiles of DNA Repair Activity Define Favorable-risk Prostate Cancer Subtypes With Divergent Outcome

INTRODUCTION

Understanding if divergent molecular profiles of DNA damage and repair (DDR) pathway activity, a biomarker of disease progression, exist in prostate tumors with favorable-risk features is an unmet need, which this study aim to unearth.

MATERIALS AND METHODS

This was a multicenter registry genome-wide expression profiling study of prospectively collected radical prostatectomy (RP) tumor samples from 2014 to 2016. DDR activity was calculated from average expression of 372 DDR genes. Consensus hierarchical clustering was used to arrive at a robust clustering solution based on DDR gene expression patterns. Genome-wide differential expression between clusters was performed, and outcomes were evaluated across expression patterns.

RESULTS

Of 5239 patients from the prospective registry, 376 had favorable-risk disease (Grade group [GG] 1 to 2, PSA prior to RP <10ng/ml, pT2 or less). DDR activity score was correlated with prognostic genomic signatures that predict for metastatic risk (r = 0.37, P < 2e^-16) and high grade groups (P < .001). High DDR activity (top-quartile) was observed in 28% of patients with favorable-risk disease. In favorable-risk disease, 3 distinct clusters with varied DDR activity emerged with consensus clustering. Cluster I (compared with cluster II-III and GG3-GG5 disease) had the highest expression of all DDR sub-pathways, MYC, PAPR1, AR, and AR activity (P < .001 for all). Furthermore, cluster I was associated with poorer metastasis-free survival (MFS) and Overall survival (OS) compared with other clusters (MFS; HR: 2.43, 95%CI, [1.22-4.83], P = .01; OS; HR: 2.77, 95%CI, [1.18-6.5], P = .01).

CONCLUSIONS

Cluster I is a novel subgroup of favorable-risk disease with high DDR activity, AR activity, PARP1 and chr8q/MYC expression, and poorer MFS and OS.

Expression and prognostic signatures of m6A-related lncRNAs in hepatocellular carcinoma

BACKGROUND

N6-methyladenosine (m6A) is a common modification and plays an important role in various biological processes, but m6A-related lncRNA functions in hepatocellular carcinoma (HCC) have not been systematically clarified.

METHODS

The clinical data and RNA-seq transcriptome of 375 cases of HCC and 50 cases of normal tissues were obtained from the Cancer Gene Atlas database. Co-expression analysis was used to obtain m6A-related lncRNA. The independent prognostic factors were identified by univariate and multivariate Cox regression models. Kaplan-Meier method was used in survival analysis. The core gene of the mRNA-mRNA interaction network is related to m6A-related lncRNAs obtained by the CytoHubba plugin of Cytoscape. Gene ontology and Kyoto Gene Encyclopedia were analyzed to find out the potential mechanism. CIBERSORT algorithm was used to calculate the relative proportion of immune infiltrating cells.

RESULTS

We identified two subgroups (cluster 1 and cluster 2) according to the expression level. The survival analysis curve and receiver operating characteristic curve proved that this model could predict the prognosis of HCC patients. The univariate and multivariate Cox regression analyses showed the independent prognostic value. UBE2C was screened as the pivotal gene. The expression level of m6A-related lncRNAs causes changes in the tumor immune microenvironment.

CONCLUSION

The expression levels of m6A-related lncRNAs were significantly different and the prognostic value of m6A-related lncRNAs was confirmed. The m6A-related lncRNAs are expected to be prognostic signatures in HCC.

N6-methyladenosine-related lncRNAs is a potential marker for predicting prognosis and immunotherapy in ovarian cancer

Background

With a lack of specific symptoms, ovarian cancer (OV) is often diagnosed at an advanced stage. This coupled with inadequate prognostic indicators and treatments with limited therapeutic effect make OV the deadliest type of gynecological tumor. Recent research indicates that N6-methyladenosine (m6A) and long-chain non-coding RNA (lncRNA) play important roles in the prognosis of OV and the efficacy of immunotherapy.

Results

Using the Cancer Genome Atlas (TCGA) OV-related data set and the expression profiles of 21 m6A-related genes, we identified two m6A subtypes, and the differentially expressed genes between the two. Based on the differentially expressed lncRNAs in the two m6A subtypes and the lncRNAs co-expressed with the 21 m6A-related genes, single-factor cox and LASSO regression were used to further isolate the 13 major lncRNAs. Finally, multi-factor cox regression was used to construct a m6A-related lncRNA risk score model for OV, with good performance in patient prognosis. Using risk score, OV tumor samples are divided into with high- and low-score groups. We explored the differences in clinical characteristics, tumor mutational burden, and tumor immune cell infiltration between the two groups, and evaluated the risk score’s ability to predict the benefit of immunotherapy.

Conclusion

Our m6A-based lncRNA risk model could be used to predict the prognosis and immunotherapy response of future OV patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-022-00222-3.

As a prognostic biomarker of clear cell renal cell carcinoma RUFY4 predicts immunotherapy responsiveness in a PDL1-related manner

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most lethal malignancies in the urinary system and the existing immunotherapy has not achieved satisfactory outcomes. Therefore, this study aims at establishing a novel gene signature for immune infiltration and clinical outcome (overall survival and immunotherapy responsiveness) in ccRCC patients.

Methods

Based on RNA sequencing data and clinical information in The Cancer Genome Atlas (TCGA) database, we calculated proportions of immune cells in 611 samples using an online tool CIBERSORTx. Multivariate survival analysis was conducted to determine crucial survival-associated immune cells and immune-infiltration-related genes (IIRGs). Next, the clinical specimens and common renal cancer cell lines were applied to confirm IIRGs expression at protein and RNA levels. Finally, functional enrichment analyses and siRNA technology targeted to RUFY4 were implemented to verify its function of predicting immunotherapy response.

Results

Follicular helper T cells (TFHs) and Regulatory T cells (Tregs) were highly infiltrated in the tumor microenvironment (TME) and their relative proportions were independent prognostic factors for patients. Among IIRGs of TFHs and TREGs, RUFY4 was found to be highly activated in tumor microenvironment and its co-expression network was enriched in PDL1/PD1 checkpoint pathway in cancer. Additionally, knockdown of RUFY4 led to the decline of PDL1 and proliferation ability in ccRCC cell lines.

Conclusion

TFHs and Tregs were considered as prognostic biomarkers and RUFY4 was an immunotherapeutic predictor of ccRCC patients in a PDL1-Related manner.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02480-7.

Impact of the EndoPredict genomic assay on treatment decisions for oestrogen receptor-positive early breast cancer patients: benefits of physician selective testing

PURPOSE

Genomic tests improve accuracy of risk prediction for early breast cancers but these are expensive. This study evaluated the clinical utility of EndoPredict®, in terms of impact on adjuvant therapy recommendations and identification of parameters to guide selective application.

METHODS

Patients with ER-positive, HER2-negative, and early-stage invasive breast cancer were tested with EndoPredict®. Two cohorts were recruited: one consecutively and another at clinical team discretion. Systemic treatment recommendations were recorded before and after EndoPredict® results were revealed to the multidisciplinary team.

RESULTS

233 patients were recruited across five sites: 123 consecutive and 110 at clinical team discretion. In the consecutive cohort 50.6% (62/123) cases were classified high risk of recurrence by EndoPredict®, compared with 62.7% (69/110) in the selective cohort. A change in treatment recommendation was significantly more likely (p < 0.0001) in the selective cohort (43/110, 39.1%) compared to the consecutive group (11/123, 8.9%). The strongest driver of selective recruitment was intermediate grade histology, whilst logistic regression modelling demonstrated that nodal status (p < 0.001), proliferative rate (p = 0.001), and progesterone receptor positivity (p < 0.001) were the strongest discriminators of risk.

CONCLUSION

Whilst molecular risk can be predicted by traditional variables in a high proportion of cases, EndoPredict® had a greater impact on treatment decisions in those cases selected for testing at team discretion. This is indicative of the robust ability of the clinical team to identify cases most likely to benefit from testing, underscoring the value of genomic tests in the oncologists' tool kit.

Impact of the menstrual cycle on commercial prognostic gene signatures in oestrogen receptor-positive primary breast cancer

PURPOSE

Changes occur in the expression of oestrogen-regulated and proliferation-associated genes in oestrogen receptor (ER)-positive breast tumours during the menstrual cycle. We investigated if Oncotype® DX recurrence score (RS), Prosigna® (ROR) and EndoPredict® (EP/EPclin) prognostic tests, which include some of these genes, vary according to the time in the menstrual cycle when they are measured.

METHODS

Pairs of test scores were derived from 30 ER-positive/human epidermal growth factor receptor-2-negative tumours sampled at two different points of the menstrual cycle. Menstrual cycle windows were prospectively defined as either W1 (days 1-6 and 27-35; low oestrogen and low progesterone) or W2 (days 7-26; high oestrogen and high or low progesterone).

RESULTS

The invasion module score of RS was lower (- 10.9%; p = 0.098), whereas the ER (+ 16.6%; p = 0.046) and proliferation (+ 7.3%; p = 0.13) module scores were higher in W2. PGR expression was significantly increased in W2 (+ 81.4%; p = 0.0029). Despite this, mean scores were not significantly different between W1 and W2 for any of the tests and the two measurements showed high correlation (r = 0.72-0.93). However, variability between the two measurements led to tumours being assigned to different risk categories in the following proportion of cases: RS 22.7%, ROR 27.3%, EP 13.6% and EPclin 13.6%.

CONCLUSION

There are significant changes during the menstrual cycle in the expression of some of the genes and gene module scores comprising the RS, ROR and EP/EPclin scores. These did not affect any of the prognostic scores in a systematic fashion, but there was substantial variability in paired measurements.

The prognostic significance of peritumoral tertiary lymphoid structures in breast cancer

Tumors and their surrounding area represent spatially organized "ecosystems", where tumor cells and the immune contextures of the different compartments are in a dynamic interplay, with potential clinical impact. Here, we aimed to investigate the prognostic significance of peritumoral tertiary lymphoid structures (TLS) either alone or jointly with the intratumoral densities and spatial distribution of CD8 + and CD163 + cells in breast cancer (BCa) patients. TLS were identified peritumorally, within the area distancing up to 5 mm from the infiltrative tumor border, counted and further characterized as adjacent or distal, in formalin-fixed, paraffin-embedded tumor tissue samples from a cohort of 167 patients, with histologically confirmed invasive ductal BCa. TLS and tumor-infiltrating immune cells were determined by H&E and immunohistochemistry. Clinical follow-up was available for 112 of these patients. Patients with peritumoral TLS exhibited worse disease-free survival (DFS) and overall survival (OS) as compared to patients lacking TLS. Moreover, the density of peritumoral TLS was found to be crucial for prognosis, since patients with abundant TLS exhibited the worst DFS and OS. By combining the density of adjacent TLS (aTLS) with our recently published intratumoral signatures based on the differential distribution of CD8 + and CD163 + in the tumor center and invasive margin, we created two improved immune signatures with superior prognostic strength and higher patient population coverage. Our observations strengthen the notion for the fundamental role of the dynamic interplay between the immune cells within the tumor microenvironment (center/invasive margin) and the tumor surrounding area (peritumoral TLS) on the clinical outcome of BCa patients.

Identification and clinical validation of a multigene assay that interrogates the biology of cancer stem cells and predicts metastasis in breast cancer: A retrospective consecutive study

Background

Breast cancers show variations in the number and biological aggressiveness of cancer stem cells that correlate with their clinico-prognostic and molecular heterogeneity. Thus, prognostic stratification of breast cancers based on cancer stem cells might help guide patient management.

Methods

We derived a 20-gene stem cell signature from the transcriptional profile of normal mammary stem cells, capable of identifying breast cancers with a homogeneous profile and poor prognosis in in silico analyses. The clinical value of this signature was assessed in a prospective-retrospective cohort of 2, 453 breast cancer patients. Models for predicting individual risk of metastasis were developed from expression data of the 20 genes in patients randomly assigned to a training set, using the ridge-penalized Cox regression, and tested in an independent validation set.

Findings

Analyses revealed that the 20-gene stem cell signature provided prognostic information in Triple-Negative and Luminal breast cancer patients, independently of standard clinicopathological parameters. Through functional studies in individual tumours, we correlated the risk score assigned by the signature with the proliferative and self-renewal potential of the cancer stem cell population. By retraining the 20-gene signature in Luminal patients, we derived the risk model, StemPrintER, which predicted early and late recurrence independently of standard prognostic factors.

Interpretation

Our findings indicate that the 20-gene stem cell signature, by its unique ability to interrogate the biology of cancer stem cells of the primary tumour, provides a reliable estimate of metastatic risk in Triple-Negative and Luminal breast cancer patients independently of standard clinicopathological parameters.