Characterization of stem cell landscape and identification of stemness-relevant prognostic gene signature to aid immunotherapy in colorectal cancer

Integrated single-cell and bulk RNA sequencing revealed an epigenetic signature predicts prognosis and tumor microenvironment colorectal cancer heterogeneity

BACKGROUND

Colorectal cancer (CRC) prognosis prediction is currently a major challenge. Epigenetic regulation has been widely reported for its role in cancer development.

AIM

To construct a robust prognostic signature, we used developed and validated across datasets.

METHODS

After constructing the signature, the prognostic value of the signature was evaluated in the TCGA cohort and six independent datasets (GSE17526, GSE17537, GSE33113, GSE37892, GSE39048 and GSE39582). The clinical, genomic and transcriptomic features related to the signature were identified. The correlations of the signature score with immune cell infiltration and cell-cell interactions were analyzed. The correlations between the signature score and the sensitivity to different drugs were also predicted.

RESULTS

In the TCGA cohort, patients in the low-risk group according to the signature score had longer survival than those in the high-risk group, and this finding was validated in the validation datasets. The signature was a prognostic factor independent of age and sex and was correlated with stage and PD-1/PD-L1 expression. Area under the receiving operating characteristic curve was 0.72. Genomic association analyses revealed that samples from high-risk patients exhibited chromosomal instability. Transcriptomic analyses revealed that the signature score was significantly associated with multiple cellular pathways. Bulk RNA-seq and single-cell sequencing data revealed that the signature reflected differences in infiltrating immune cell-tumor cell interactions, especially for macrophages. The signature also predicted the putative drug sensitivity of CRC samples.

CONCLUSION

The signature is a valuable biomarker for predicting CRC prognosis and reflects multiple features of CRC, especially macrophage infiltration in the microenvironment.

Identification and validation of a novel 17 coagulation-related genes signature for predicting prognostic risk in colorectal cancer

Background

Patients with colorectal cancer commonly experience disturbances in coagulation homeostasis. Activation of the coagulation system contributes to cancer-associated thrombosis as the second risk factor for death in cancer patients. This study intended to discover coagulation-related genes and construct a risk model for colorectal cancer patients' prognosis.

Methods

Coagulation-related genes were identified by searching coagulation-related pathways in the Molecular Signatures Database. Transcriptomic data and clinical data were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus datasets. Univariate Cox and backward stepwise regression were utilized to identify prognosis-related genes and construct a predictive risk model for the training cohort. Next, survival analysis determines the risk model's predictive power, correlation with clinicopathological characteristics, and nomogram. Additionally, we characterized the variances in immune cell infiltration, somatic mutations, immune checkpoint molecules, biological functions, and drug sensitivity between the high- and low-score patients.

Result

Eight hundred forty-five genes were obtained by searching the theme term "coagulation" after de-duplication. After univariate regression analysis, 69 genes correlated with prognosis were obtained from the Cancer Genome Atlas dataset. A signature consisting of 17 coagulation-related genes was established through backward stepwise regression. The Kaplan-Meier curve indicated a worse prognosis for high-score patients. Time-dependent receiver operating characteristic curve analysis demonstrated high accuracy in predicting overall survival. Further, the results were validated by two independent datasets (GSE39582 and GSE17536). Combined with clinicopathological characteristics, the risk model was proven to be an independent prognostic factor to predict poor pathological status and worse prognosis. Furthermore, high-score patients had significantly higher stromal cell infiltration. Low-score patients were associated with high infiltration of resting memory CD4+ T cells, activated CD4+ T cells, and T follicular helper cells. The low-score patients exhibited increased expression of immune checkpoint genes, and this might be relevant to their better prognosis. High-score patients exhibited lower IC50 values of Paclitaxel, Rapamycin, Temozolomide, Cyclophosphamide, etc. The differential signaling pathways mainly involve the calcium signaling pathway and the neuroactive ligand-receptor interaction. Lastly, a nomogram was constructed and showed a good prediction.

Conclusion

The prognostic signature of 17 coagulation-related genes had significant prognostic value for colorectal cancer patients. We expect to improve treatment modalities and benefit more patients through research on molecular features.

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.

Integrated multi-omics analyses revealed the association between rheumatoid arthritis and colorectal cancer: MYO9A as a shared gene signature and an immune-related therapeutic target

BACKGROUND

Our study aims to explore the relationship, shared gene signature, and the underlying mechanisms that connect rheumatoid arthritis (RA) to colorectal cancer (CRC).

METHODS

Mendelian randomization (MR) analysis was conducted to assess the causality between RA and CRC. Summary statistic data-based Mendelian randomization (SMR) leveraging eQTL data was employed to identify the CRC-related causal genes. Integrated analyses of single-cell RNA sequencing and bulk RNA sequencing were employed to comprehensively investigate the shared gene signature and potential mechanisms underlying the pathogenesis of both RA and CRC. Predictive analysis of the shared hub gene in CRC immunotherapy response was performed. Pan-cancer analyses were conducted to explore the potential role of MYO9A in 33 types of human tumors.

RESULTS

MR analysis suggested that RA might be associated with a slight increased risk of CRC (Odds Ratio = 1.04, 95% Confidence Interval = 1.01-1.07, P = 0.005). SMR analysis combining transcriptome analyses identified MYO9A as a causal gene in CRC and a shared gene signature in both RA and CRC. MYO9A may contribute to tumor suppression, while downregulation of MYO9A may impact CRC tumorigenesis by disrupting epithelial polarity and architecture, resulting in a worse prognosis in CRC. Additionally, MYO9A shows promise as a powerful predictive biomarker for cancer prognosis and immunotherapy response in CRC. Pan-cancer analyses demonstrated MYO9A may have a protective role in the occurrence and progression of various human cancers.

CONCLUSION

RA might be associated with a slight increased risk of CRC. MYO9A is a shared gene signature and a potential immune-related therapeutic target for both CRC and RA. Targeting the MYO9A-mediated loss of polarity and epithelial architecture could be a novel therapeutic approach for CRC.

Discovery of core genes and intercellular communication role in osteosarcoma

Osteosarcoma is a primary malignant bone tumor that affects children and young adults. Understanding the molecular mechanisms underlying osteosarcoma is critical to develop effective treatments. This study aimed to identify core genes and explore the role of intercellular communication in osteosarcoma. We used GSE87437 and GSE152048 dataset to conduct a weighted correlation network analysis (WGCNA) and identify co-expression modules. The enriched biological processes and cellular components of the genes in the steelblue module were analyzed. Next, we explored the expression, diagnostic value, correlation, and association with immune infiltrate of CCSER1 and LOC101929154. Finally, we utilized CIBERSORT algorithm to predict the infiltrated immune cells in osteosarcoma tissues. Our results identified 44 co-expression modules, and the steelblue module was mainly associated with axon development, axonogenesis, and innervation. CCSER1 and LOC101929154 were significantly upregulated in osteosarcoma tissues with poor response to preoperative chemotherapy. Moreover, the expressions of CCSER1 and LOC101929154 were positively correlated. The area under the receiver operating characteristic curve of CCSER1 and LOC101929154 was 0.800 and 0.773, respectively. The expression of CCSER1 was negatively correlated with follicular helper T cells and positively correlated with M0 macrophages, while LOC101929154 was negatively correlated with activated mast cells. Besides, CD4 memory-activated T cells were observed at lower levels in patients who responded well to chemotherapy. Our study identified core genes CCSER1 and LOC101929154 and provided insight into the intercellular communication profile in osteosarcoma. Our results suggested that targeting CCSER1, LOC101929154, and CD4 memory-activated T cells may be a promising strategy for the treatment of osteosarcoma.

DDIT3 is associated with breast cancer prognosis and immune microenvironment: an integrative bioinformatic and immunohistochemical analysis

DNA damage-inducible transcript 3 (DDIT3) is a transcription factor central to apoptosis, differentiation, and stress response. DDIT3 has been extensively studied in cancer biology. However, its precise implications in breast cancer progression and its interaction with the immune microenvironment are unclear. In this study, we utilized a novel multi-omics integration strategy, combining bulk RNA sequencing, single-cell sequencing, spatial transcriptomics and immunohistochemistry, to explore the role of DDIT3 in breast cancer and establish the correlation between DDIT3 and poor prognosis in breast cancer patients. We identified a robust prognostic signature, including six genes (unc-93 homolog B1, TLR signaling regulator, anti-Mullerian hormone, DCTP pyrophosphatase 1, mitochondrial ribosomal protein L36, nuclear factor erythroid 2, and Rho GTPase activating protein 39), associated with DDIT3. This signature stratified the high-risk patient groups, characterized by increased infiltration of the regulatory T cells and M2-like macrophages and fibroblast growth factor (FGF)/FGF receptor signaling activation. Notably, the high-risk patient group demonstrated enhanced sensitivity to immunotherapy, presenting novel therapeutic opportunities. Integrating multi-omics data helped determine the spatial expression pattern of DDIT3 in the tumor microenvironment and its correlation with immune cell infiltration. This multi-dimensional analysis provided a comprehensive understanding of the intricate interplay between DDIT3 and the immune microenvironment in breast cancer. Overall, our study not only facilitates understanding the role of DDIT3 in breast cancer but also offers innovative insights for developing prognostic models and therapeutic strategies. Identifying the DDIT3-related prognostic signature and its association with the immune microenvironment provided a promising avenue for personalized breast cancer treatment.

Obesity-Dependent Association of the rs10454142 PPP1R21 with Breast Cancer

The purpose of this work was to find a link between the breast cancer (BC)-risk effects of sex hormone-binding globulin (SHBG)-associated polymorphisms and obesity. The study was conducted on a sample of 1498 women (358 BC; 1140 controls) who, depending on the presence/absence of obesity, were divided into two groups: obese (119 BC; 253 controls) and non-obese (239 BC; 887 controls). Genotyping of nine SHBG-associated single nucleotide polymorphisms (SNP)-rs17496332 PRMT6, rs780093 GCKR, rs10454142 PPP1R21, rs3779195 BAIAP2L1, rs440837 ZBTB10, rs7910927 JMJD1C, rs4149056 SLCO1B1, rs8023580 NR2F2, and rs12150660 SHBG-was executed, and the BC-risk impact of these loci was analyzed by logistic regression separately in each group of obese/non-obese women. We found that the BC-risk effect correlated by GWAS with the SHBG-level polymorphism rs10454142 PPP1R21 depends on the presence/absence of obesity. The SHBG-lowering allele C rs10454142 PPP1R21 has a risk value for BC in obese women (allelic model: CvsT, OR = 1.52, 95%CI = 1.10-2.11, and pperm = 0.013; additive model: CCvsTCvsTT, OR = 1.71, 95%CI = 1.15-2.62, and pperm = 0.011; dominant model: CC + TCvsTT, OR = 1.95, 95%CI = 1.13-3.37, and pperm = 0.017) and is not associated with the disease in women without obesity. SNP rs10454142 PPP1R21 and 10 proxy SNPs have adipose-specific regulatory effects (epigenetic modifications of promoters/enhancers, DNA interaction with 51 transcription factors, eQTL/sQTL effects on five genes (PPP1R21, RP11-460M2.1, GTF2A1L, STON1-GTF2A1L, and STON1), etc.), can be "likely cancer driver" SNPs, and are involved in cancer-significant pathways. In conclusion, our study detected an obesity-dependent association of the rs10454142 PPP1R21 with BC in women.

Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma

BACKGROUND

Fatty acids synthesis and metabolism (FASM)-driven lipid mobilization is essential for energy production during nutrient shortages. However, the molecular characteristics, physiological function and clinical prognosis value of FASM-associated gene signatures in hepatocellular carcinoma (HCC) remain elusive.

METHODS

The Gene Expression Omnibus database (GEO), the Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database were utilized to acquire transcriptome data and clinical information of HCC patients. The ConsensusClusterPlus was employed for unsupervised clustering. Subsequently, immune cell infiltration, stemness index and therapeutic response among distinct clusters were decoded. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to anticipate the response of patients towards immunotherapy, and the genomics of drug sensitivity in cancer (GDSC) tool was employed to predict their response to antineoplastic medications. Least absolute shrinkage and selection operator (LASSO) regression analysis and protein-protein interaction (PPI) network were employed to construct prognostic model and identity hub gene. Single cell RNA sequencing (scRNA-seq) and CellChat were used to analyze cellular interactions. The hub gene of FASM effect on promoting tumor progression was confirmed through a series of functional experiments.

RESULTS

Twenty-six FASM-related genes showed differential expression in HCC. Based on these FASM-related differential genes, two molecular subtypes were established, including Cluster1 and Cluster2 subtype. Compared with cluster2, Cluster1 subtype exhibited a worse prognosis, higher risk, higher immunosuppressive cells infiltrations, higher immune escape, higher cancer stemness and enhanced treatment-resistant. PPI network identified Acetyl-CoA carboxylase1 (ACACA) as central gene of FASM and predicted a poor prognosis. A strong interaction between cancer stem cells (CSCs) with high expression of ACACA and macrophages through CD74 molecule (CD74) and integrin subunit beta 1 (ITGB1) signaling was identified. Finally, increased ACACA expression was observed in HCC cells and patients, whereas depleted ACACA inhibited the stemness straits and drug resistance of HCC cells.

CONCLUSIONS

This study provides a resource for understanding FASM heterogeneity in HCC. Evaluating the FASM patterns can help predict the prognosis and provide new insights into treatment response in HCC patients.

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.

Tumor cell stemness in gastrointestinal cancer: regulation and targeted therapy

The cancer stem cells are a rare group of self-renewable cancer cells capable of the initiation, progression, metastasis and recurrence of tumors, and also a key contributor to the therapeutic resistance. Thus, understanding the molecular mechanism of tumor stemness regulation, especially in the gastrointestinal (GI) cancers, is of great importance for targeting CSC and designing novel therapeutic strategies. This review aims to elucidate current advancements in the understanding of CSC regulation, including CSC biomarkers, signaling pathways, and non-coding RNAs. We will also provide a comprehensive view on how the tumor microenvironment (TME) display an overall tumor-promoting effect, including the recruitment and impact of cancer-associated fibroblasts (CAFs), the establishment of an immunosuppressive milieu, and the induction of angiogenesis and hypoxia. Lastly, this review consolidates mainstream novel therapeutic interventions targeting CSC stemness regulation.

LGR5, a prognostic stem cell target, promotes endometrial cancer proliferation through autophagy activation

Endometrial cancer (EC) is a common malignant tumor in women worldwide. Although early EC has a good prognosis, advanced endometrial cancer is still associated with the risk of drug resistance and recurrence. Cancer stem cells (CSCs), a category closely related to drug resistance and recurrence, are rarely studied at present. Here, we constructed a risk model containing ten stemness-related prognostic genes. Compared with patients in the low-risk group, patients in the high-risk group had a shorter overall survival time. The accuracy of this model was verified by ROC in the TCGA (AUC = 0.779) and Peking University People's Hospital (PKUPH, AUC = 0.864) cohorts. The risk score and stage were independent risk factors in the multivariate regression analysis, which was subsequently used to construct the nomogram and verified in the TCGA cohort. LGR5 was significantly correlated with overall survival and involvement in the Wnt signaling pathway. In addition, LGR5 was highly expressed in EC tissues and was related to age, stage, histological type, and menopause status in the TCGA database. Overexpression of LGR5 accelerated the proliferation rate of EC cells, which may be related to autophagy activation. Taken together, our study established a prognostic model based on transcription sequencing data from the TCGA database and verified it in the PKUPH cohort, which has prospective clinical implications for the prognostic evaluation of EC. We systematically studied the code gene LGR5 in EC, which may help clinicians make personalized prognostic assessments and effective clinical decisions for EC.

Weighted gene co-expression network analysis for hub genes in colorectal cancer

BACKGROUND

This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA).

METHODS

Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established.

RESULTS

After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC.

CONCLUSIONS

CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.

Complement and coagulation cascades are associated with prognosis and the immune microenvironment of lower-grade glioma

Background

Abnormal coagulation is a common feature of glioma. There is a strong correlation between coagulation and the complement system, named complement and coagulation cascades (CCC). However, the role of CCC genes in lower-grade glioma (LGG) remains unclear. This study aimed to investigate the role of CCC genes in LGG.

Methods

In total, 5,628 differential expressed genes were identified between 498 LGG tissues from The Cancer Genome Atlas (TCGA) and 207 normal brain tissues from Genotype-Tissue Expression Project (GTEx). Among them, 20 overlapped CCC genes were identified as differentially expressed CCC genes. Then, comprehensive bioinformatics analysis was used to investigate the role of CCC genes in LGG; 271 LGG tissues from the Chinese Glioma Genome Atlas (CGGA) were used as the validation dataset. Cell Counting Kit-8 (CCK8) proliferation assay, colony formation assay, and wound healing assay were conducted to explore the anti-glioma effect of the sensitive drugs we predicted.

Results

We constructed a risk signature consisting of six CCC genes, including F2R, SERPINA1, TFPI, C1QC, C2, and C3AR1. The CCC gene-based risk signature could accurately predict the prognosis of patients with LGG. In addition, we found that the JAK-STAT, NOD-like receptor, Notch, PI3K-Akt, and Rap1 signaling pathways might be activated and had crosstalk with CCC in the high-risk group. Our findings analyses demonstrated that samples in high- and low-risk groups had different immune landscapes. Moreover, patients in the high-risk group might have greater resistance to immunotherapy. We validated the accuracy of the risk signature in predicting immunotherapy response in two public immunotherapy cohorts, GSE135222 and GSE78220. By means of oncoPredict, MG-132, BMS-536924, PLX-4720, and AZD6482 were identified as potential sensitive drugs for high-risk patients, of which MG-132 was particularly recommended for high-risk patients. We performed in vitro experiments to explore the anti-glioma effect of MG-132, and the results demonstrated MG-132 could inhibit the proliferation and migration of glioma cells.

Conclusions

Our findings show that CCC genes are associated with the prognosis and immune infiltration of LGG and provide possible immunotherapeutic and novel chemotherapeutic strategies for patients with LGG based on the risk signature.

Identification of gut microbes-related molecular subtypes and their biomarkers in colorectal cancer

The role of gut microbes (GM) and their metabolites in colorectal cancer (CRC) development has attracted increasing attention. Several studies have identified specific microorganisms that are closely associated with CRC occurrence and progression, as well as key genes associated with gut microorganisms. However, the extent to which gut microbes-related genes can serve as biomarkers for CRC progression or prognosis is still poorly understood. This study used a bioinformatics-based approach to synthetically analyze the large amount of available data stored in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Through this analysis, this study identified two distinct CRC molecular subtypes associated with GM, as well as CRC markers related to GM. In addition, these new subtypes exhibit significantly different survival outcomes and are characterized by distinct immune landscapes and biological functions. Gut microbes-related biomarkers (GMRBs), IL7 and BCL10, were identified and found to have independent prognostic value and predictability for immunotherapeutic response in CRC patients. In addition, a systematic collection and review of prior research literature on GM and CRC provided additional evidence to support these findings. In conclusion, this paper provides new insights into the underlying pathological mechanisms by which GM promotes the development of CRC and suggests potentially viable solutions for individualized prevention, screening, and treatment of CRC.

The oncogenic mechanisms of the Janus kinase-signal transducer and activator of transcription pathway in digestive tract tumors

Digestive tract tumors are heterogeneous and involve the dysregulation of multiple signaling pathways. The Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway plays a notable role in the oncogenesis of digestive tract tumors. Typically activated by pro-inflammatory cytokines, it regulates important biological processes, such as cell growth, differentiation, apoptosis, immune responses, and inflammation. The aberrant activation of this pathway manifests in different forms, including mutations in JAKs, overexpression of cytokine receptors, and sustained STAT activation, and contributes to promoting the malignant characteristics of cancer cells, including uncontrolled proliferation, resistance to apoptosis, enhanced invasion and metastasis, angiogenesis, acquisition of stem-like properties, and drug resistance. Numerous studies have shown that aberrant activation of the JAK-STAT pathway is closely related to the development and progression of digestive tract tumors, contributing to tumor survival, angiogenesis, changes in the tumor microenvironment, and even immune escape processes. In addition, this signaling pathway also affects the sensitivity of digestive tract tumors to chemotherapy and targeted therapy. Therefore, it is crucial to comprehensively understand the oncogenic mechanisms underlying the JAK-STAT pathway in order to develop effective therapeutic strategies against digestive tract tumors. Currently, several JAK-STAT inhibitors are undergoing clinical and preclinical trials as potential treatments for various human diseases. However, further investigation is required to determine the role of this pathway, as well as the effectiveness and safety of its inhibitors, especially in the context of digestive tract tumors. In this review, we provide an overview of the structure, classic activation, and negative regulation of the JAK-STAT pathway. Furthermore, we discuss the pathogenic mechanisms of JAK-STAT signaling in different digestive tract tumors, with the aim of identifying potential novel therapeutic targets. Video Abstract.

An individualized stemness-related signature to predict prognosis and immunotherapy responses for gastric cancer using single-cell and bulk tissue transcriptomes

BACKGROUND

Currently, many stemness-related signatures have been developed for gastric cancer (GC) to predict prognosis and immunotherapy outcomes. However, due to batch effects, these signatures cannot accurately analyze patients one by one, rendering them impractical in real clinical scenarios. Therefore, we aimed to develop an individualized and clinically applicable signature based on GC stemness.

METHODS

Malignant epithelial cells from single-cell RNA-Seq data of GC were used to identify stemness-related signature genes based on the CytoTRACE score. Using two bulk tissue datasets as training data, the enrichment scores of the signature genes were applied to classify samples into two subtypes. Then, using the identified subtypes as criteria, we developed an individualized stemness-related signature based on the within-sample relative expression orderings of genes.

RESULTS

We identified 175 stemness-related signature genes, which exhibited significantly higher AUCell scores in poorly differentiated GCs compared to differentiated GCs. In training datasets, GC samples were classified into two subtypes with significantly different survival times and genomic characteristics. Utilizing the two subtypes, an individualized signature was constructed containing 47 gene pairs. In four independent testing datasets, GC samples classified as high risk exhibited significantly shorter survival times, higher infiltration of M2 macrophages, and lower immune responses compared to low-risk samples. Moreover, the potential therapeutic targets and corresponding drugs were identified for the high-risk group, such as CD248 targeted by ontuxizumab.

CONCLUSIONS

We developed an individualized stemness-related signature, which can accurately predict the prognosis and efficacy of immunotherapy for each GC sample.

Systematic Analysis of Tumor Stem Cell-related Gene Characteristics to Predict the PD-L1 Immunotherapy and Prognosis of Gastric Cancer

AIMS

We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in gastric cancer (GC).

BACKGROUND

Tumor stemness is related to intratumoral heterogeneity, immunosuppression, and anti-tumor resistance. We developed a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.

OBJECTIVE

We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.

METHODS

We downloaded single-cell RNA sequencing (scRNA-seq) data of GC patients from the Gene-Expression Omnibus (GEO) database and screened GC stemness- related genes using CytoTRACE. We characterized the association of tumor stemness with immune checkpoint blockade (ICB) and immunity. Thereafter, a 9-stemness signature-based prognostic model was developed using weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis. The model predictive value was evaluated with a nomogram.

RESULTS

Early GC patients had significantly higher levels of stemness. The stemness score showed a negative relationship to tumor immune dysfunction and exclusion (TIDE) score and immune infiltration, especially T cells and B cells. A stemness-based signature based on 9 genes (ERCC6L, IQCC, NKAPD1, BLMH, SLC25A15, MRPL4, VPS35, SUMO3, and CINP) was constructed with good performance in prognosis prediction, and its robustness was validated in GSE26942 cohort. Additionally, nomogram and risk score exhibited the most powerful ability for prognosis prediction. High-risk patients exhibited a tendency to develop immune escape and low response to PD-L1 immunotherapy.

CONCLUSION

We developed a stemness-based gene signature for prognosis prediction with accuracy and reliability. This signature also helps clinical decision-making of immunotherapy for GC patients.

Molecular Biomarkers and Signaling Pathways of Cancer Stem Cells in Colorectal Cancer

Colorectal cancer (CRC) is the third most frequently found cancer in the world, and it is frequently discovered when it is already far along in its development. About 20% of cases of CRC are metastatic and incurable. There is more and more evidence that colorectal cancer stem cells (CCSCs), which are in charge of tumor growth, recurrence, and resistance to treatment, are what make CRC so different. Because we know more about stem cell biology, we quickly learned about the molecular processes and possible cross-talk between signaling pathways that affect the balance of cells in the gut and cancer. Wnt, Notch, TGF-β, and Hedgehog are examples of signaling pathway members whose genes may change to produce CCSCs. These genes control self-renewal and pluripotency in SCs and then decide the function and phenotype of CCSCs. However, in terms of their ability to create tumors and susceptibility to chemotherapeutic drugs, CSCs differ from normal stem cells and the bulk of tumor cells. This may be the reason for the higher rate of cancer recurrence in patients who underwent both surgery and chemotherapy treatment. Scientists have found that a group of uncontrolled miRNAs related to CCSCs affect stemness properties. These miRNAs control CCSC functions like changing the expression of cell cycle genes, metastasis, and drug resistance mechanisms. CCSC-related miRNAs mostly control signal pathways that are known to be important for CCSC biology. The biomarkers (CD markers and miRNA) for CCSCs and their diagnostic roles are the main topics of this review study.

New ruthenium-xanthoxylin complex eliminates colorectal cancer stem cells by targeting the heat shock protein 90 chaperone

In this work, we describe a novel ruthenium-xanthoxylin complex, [Ru(phen)2(xant)](PF6) (RXC), that can eliminate colorectal cancer (CRC) stem cells by targeting the chaperone Hsp90. RXC exhibits potent cytotoxicity in cancer cell lines and primary cancer cells, causing apoptosis in HCT116 CRC cells, as observed by cell morphology, YO-PRO-1/PI staining, internucleosomal DNA fragmentation, mitochondrial depolarization, and PARP cleavage (Asp214). Additionally, RXC can downregulate the HSP90AA1 and HSP90B1 genes and the expression of HSP90 protein, as well as the expression levels of its downstream/client elements Akt1, Akt (pS473), mTOR (pS2448), 4EBP1 (pT36/pT45), GSK-3β (pS9), and NF-κB p65 (pS529), implying that these molecular chaperones can be molecular targets for RXC. Moreover, this compound inhibited clonogenic survival, the percentage of the CRC stem cell subpopulation, and colonosphere formation, indicating that RXC can eliminate CRC stem cells. RXC reduced cell migration and invasion, decreased vimentin and increased E-cadherin expression, and induced an autophagic process that appeared to be cytoprotective, as autophagy inhibitors enhanced RXC-induced cell death. In vivo studies showed that RXC inhibits tumor progression and experimental metastasis in mice with CRC HCT116 cell xenografts. Taken together, these results highlight the potential of the ruthenium complex RXC in CRC therapy with the ability to eliminate CRC stem cells by targeting the chaperone Hsp90.

Colorectal Cancer Stem Cells and Targeted Agents

Since their discovery, cancer stem cells have become a hot topic in cancer therapy research. These cells possess stem cell-like self-renewal and differentiation capacities and are important factors that dominate cancer metastasis, therapy-resistance and recurrence. Worse, their inherent characteristics make them difficult to eliminate. Colorectal cancer is the third-most common cancer and the second leading cause of cancer death worldwide. Targeting colorectal cancer stem cells (CR-CSCs) can inhibit colorectal cancer metastasis, enhance therapeutic efficacy and reduce recurrence. Here, we introduced the origin, biomarker proteins, identification, cultivation and research techniques of CR-CSCs, and we summarized the signaling pathways that regulate the stemness of CR-CSCs, such as Wnt, JAK/STAT3, Notch and Hh signaling pathway. In addition to these, we also reviewed recent anti-CR-CSC drugs targeting signaling pathways, biomarkers and other regulators. These will help researchers gain insight into the current agents targeting to CR-CSCs, explore new cancer drugs and propose potential therapies.

Bioinformatics analysis-based screening of circRNA gene with mainstream expression trend in colorectal cancer and construction of a coexpression regulatory network

OBJECTIVE

Since circRNA can be utilized as a potential diagnostic marker for cancer, to explore the regulatory mechanism of colorectal cancer (CRC) using bioinformatics, the public database of circRNA was mined.

METHODS

CRC differentially expressed miRNAs were screened in the Cancer Genome Atlas (TCGA) database, CRC differentially expressed circRNAs were searched in the Gene Expression Omnibus (GEO) database, the two databases were combined to identify CRC differentially expressed mRNAs, and a circRNA-miRNA‒mRNA regulatory network was constructed by combining a plurality of target prediction databases to identify key genes. The upstream circRNA and regulatory axis of the key genes were identified for gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis to explore the biological functions of circRNA in CRC using the regulatory axis.

RESULTS

After the screening of the GSE21815 dataset, a total of 22 differentially expressed circRNAs were obtained, with 12 upregulated and 10 downregulated genes. Similarly, the GSE126094 dataset yielded 104 differentially expressed circRNAs, comprising 56 upregulated and 48 downregulated genes. Among the differentially expressed circRNAs, five were identified, with VDAC3 and SETD2 showing downregulated expression, while RAD23B, RPPH1, and MYBL2 exhibited upregulated expression. Following the selection process, five DEcircRNAs, eight target miRNAs, and 105 target DEmRNAs were identified. The protein-protein interaction (PPI) network revealed close relationships among the mRNAs, with E2F2, E2F3, CCND1, TNRC6A, and KAT2B identified as key genes. Notably, CCND1 emerged as a critical gene in the PPI network. Through the upregulation of has-circ-0087862, which binds to miR-892b, the translation inhibition of CCND1 by miR-892b was attenuated, leading to enhanced CCND1 expression. Functional enrichment analysis indicated that CCND1 was involved in protein binding and positive regulation of cellular processes, among other functions.

CONCLUSION

The differentially expressed genes (DEGs) in CRC markedly affected the survival time of patients. CircRNAs could be utilized as diagnostic markers of CRC, and the key genes in CRC could be screened out by bioinformatics, which would be helpful to understand the drug targets for the treatment of human immunodeficiency virus (HIV)-related CRC patients.

A pan-cancer analysis of anti-proliferative protein family genes for therapeutic targets in cancer

The recently discovered APRO (anti-proliferative protein) family encodes a group of trans-membrane glycoproteins and includes 6 members: TOB1, TOB2, BTG1, BTG2, BTG3 and BTG4. The APRO family is reportedly associated with the initiation and progression of cancers. This study aims to undertake a comprehensive investigation of the APRO family of proteins as a prognostic biomarker in various human tumors. We performed a pan-cancer analysis of the APRO family based on The Cancer Genome Atlas (TCGA). With the bioinformatics methods, we explored the prognostic value of the APRO family and the correlation between APRO family expression and tumor mutation burden (TMB), microsatellite instability (MSI), drug sensitivity, and immunotherapy in numerous cancers. Our results show that the APRO family was primarily down-regulated in cancer samples. The expression of APRO family members was linked with patient prognosis. In addition, APRO family genes showed significant association with immune infiltrate subtypes, tumor microenvironment, and tumor cell stemness. Finally, our study also demonstrated the relationship between APRO family genes and drug sensitivity. This study provides comprehensive information to understand the APRO family's role as an oncogene and predictor of survival in some tumor types.

Prognostic clinical phenotypes associated with tumor stemness in the immune microenvironment of T-cell exhaustion for hepatocellular carcinoma

T-cell exhaustion (TEX) and high heterogeneity of cancer stem cells (CSCs) are associated with progression, metastasis, and treatment resistance in hepatocellular carcinoma (HCC). Here, we aim to characterize TEX-stemness-related genes (TEXSRGs) and screen for HCC patients who are more sensitive to immunotherapy. The immune cell abundance identifier (ImmuCellAI) was utilized to precisely evaluate the abundance of TEX and screen TEX-related genes. The stemness index (mRNAsi) of samples was analyzed through the one-class logistic regression (OCLR) algorithm. Application of the non-negative matrix decomposition algorithm (NMF) for subtype identification of HCC samples. The different subtypes were assessed for differences in prognosis, tumor microenvironment (TME) landscape, and immunotherapy treatment response. Then, the TEXSRGS-score, which can accurately forecast the survival outcome of HCC patients, was built by LASSO-Cox and multivariate Cox regression, and experimentally validated for the most important TEXSRGs. We also analyzed the expression of TEXSRGs and the infiltration of CD8+ T cells in clinical samples using qRT-PCR and immunohistochemistry (IHC). Based on 146 TEXSRGs, we found two distinct clinical phenotypes with different TEX infiltration abundance, tumor stemness index, enrichment pathways, mutational landscape, and immune cell infiltration through the non-negative matrix decomposition algorithm (NMF), which were confirmed in the ICGC dataset. Utilizing eight TEXSRGs linked to clinical outcome, we created a TEXSRGs-score model to further improve the clinical applicability. Patients can be divided into two groups with substantial differences in the characteristics of immune cell infiltration, TEX infiltration abundance, and survival outcomes. The results of qRT-PCR and IHC analysis showed that PAFAH1B3, ZIC2, and ESR1 were differentially expressed in HCC and normal tissues and that patients with high TEXSRGs-scores had higher TEX infiltration abundance and tumor stemness gene expression. Regarding immunotherapy reaction and immune cell infiltration, patients with various TEXSRGs-score levels had various clinical traits. The outcome and immunotherapy efficacy of patients with low TEXSRGs-score was favorable. In conclusion, we identified two clinical subtypes with different prognoses, TEX infiltration abundance, tumor cell stemness index, and immunotherapy response based on TEXSRGs, and developed and validated a TEXSRGs-score capable of accurately predicting survival outcomes in HCC patients by comprehensive bioinformatics analysis. We believe that the TEXSRGs-score has prospective clinical relevance for prognostic assessment and may help physicians select prospective responders in preference to current immune checkpoint inhibitors (ICIs).

Characterization of stem cell subtypes and prognostic signature in hepatocellular carcinoma

BACKGROUND

Cancer stem cells (CSCs) were linked to cancer aggressiveness and poor prognosis in patients with hepatocellular carcinoma (HCC).

METHODS

We integrated two external HCC cohorts to develop the stem cell subtypes according to unsupervised clustering with 26 stem cell gene sets. Between the subtypes, differences in prognosis, clinical characteristics, recognized HCC subtypes, metabolic profile, immune-related features, somatic mutation, and drug sensitivity were examined. The prognostic signature was created, and validated by numerous cohorts, and used to assess the efficacy of immunotherapy and transcatheter arterial chemoembolization (TACE) treatment. The nomogram was developed based on the signature and clinical features. We further examined the function of KIF20A in HCC and proved that KIF20A had the potential to regulate the stemness of HCC cells through western blot.

RESULTS

Low stem cell patterns, a good prognosis, positive clinical features, specific molecular subtypes, low metastatic characteristics, and an abundance of metabolic and immunological aspects were associated with Cluster 1, whereas Cluster 2 was the reverse. Chemotherapy and immunotherapy were more effective in Cluster 1. Cluster 1 and CTNNB1 and ALB mutation were more closely. Additionally, the prognosis, immunotherapeutic, and TACE therapy responses were all worse in the high-risk group. The nomogram could predict the survival probability of HCC patients. KIF20A was discovered to be overexpressed in HCC and was revealed to be connected to the stemness of the HepG2 cell line.

CONCLUSIONS

Two stem cell subgroups with different prognoses, metabolic, and immunological characteristics in HCC patients were identified. We also created a 7-gene prognostic signature and a nomogram to estimate the survival probability. The function of KIF20A in HCC stemness was initially examined.

Immune-related gene-based model predicts the survival of colorectal carcinoma and reflected various biological statuses

Bakcground: Prognosis of colorectal cancer (CRC) varies due to complex genetic-microenviromental interactions, and multiple gene-based prognostic models have been highlighted. Material and Method: In this work, the immune-related genes' expression-based model was developed and the scores of each sample were calculated. The correlation between the model and clinical information, immune infiltration, drug response and biological pathways were analyzed. Results: The high-score samples have a significantly longer survival (overall survival and progression-free survival) period than those with a low score, which was validated across seven datasets containing 1,325 samples (GSE17536 (N = 115), GSE17537 (N = 55), GSE33113 (N = 90), GSE37892 (N = 130), GSE38832 (N = 74), GSE39582 (N = 481), and TCGA (N = 380)). The score is significantly associated with clinical indicators, including age and stage, and further associated with PD-1/PD-L1 gene expression. Furthermore, high-score samples have significantly higher APC and a lower MUC5B mutation rate. The high-score samples show more immune infiltration (including CD4+ and CD8+ T cells, M1/M2 macrophages, and NK cells). Enriched pathway analyses showed that cancer-related pathways, including immune-related pathways, were significantly activated in high-score samples and that some drugs have significantly lower IC50 values than those with low score. Conclusion: The model developed based on immune-related genes is robust and reflected various statuses of CRC and may be a potential clinical indicator.

Identification of a brand intratumor microbiome signature for predicting prognosis of hepatocellular carcinoma

PURPOSE

Given that prognosis of hepatocellular carcinoma (HCC) differs dramatically, it is imperative to uncover effective and available prognostic biomarker(s). The intratumor microbiome plays a significant role in the response to tumor microenvironment, we aimed to identify an intratumor microbiome signature for predicting the prognosis of HCC patients accurately and investigate its possible mechanisms subsequently.

METHODS

The TCGA HCC microbiome data (TCGA-LIHC-microbiome) was downloaded from cBioPortal. To create an intratumor microbiome-related prognostic signature, univariate and multivariate Cox regression analyses were used to quantify the association of microbial abundance and patients' overall survival (OS), as well as their diseases specific survival (DSS). The performance of the scoring model was evaluated by the area under the ROC curve (AUC). Based on the microbiome-related signature, clinical factors, and multi-omics molecular subtypes on the basis of "icluster" algorithm, nomograms were established to predict OS and DSS. Patients were further clustered into three subtypes based on their microbiome-related characteristics by consensus clustering. Moreover, deconvolution algorithm, weighted correlation network analysis (WGCNA) and gene set variation analysis (GSVA) were used to investigate the potential mechanisms.

RESULTS

In TCGA LIHC microbiome data, the abundances of 166 genera among the total 1406 genera were considerably associated with HCC patients' OS. From that filtered dataset we identified a 27-microbe prognostic signature and developed a microbiome-related score (MRS) model. Compared with those in the relatively low-risk group, patients in higher-risk group own a much worse OS (P < 0.0001). Besides, the time-dependent ROC curves with MRS showed excellent predictive efficacy both in OS and DSS. Moreover, MRS is an independent prognostic factor for OS and DSS over clinical factors and multi-omics-based molecular subtypes. The integration of MRS into nomograms significantly improved the efficacy of prognosis prediction (1-year AUC:0.849, 3-year AUC: 0.825, 5-year AUC: 0.822). The analysis of microbiome-based subtypes on their immune characteristics and specific gene modules inferred that the intratumor microbiome may affect the HCC patients' prognosis via modulating the cancer stemness and immune response.

CONCLUSION

MRS, a 27 intratumor microbiome-related prognostic model, was successfully established to predict HCC patients overall survive independently. And the possible underlying mechanisms were also investigated to provide a potential intervention strategy.

The prognostic and biological importance of chromatin regulation-related genes for lung cancer is examined using bioinformatics and experimentally confirmed

BACKGROUND

The pathogenesis and clinical diagnosis of lung adenocarcinoma (LUAD), a malignant illness with substantial morbidity and mortality, are still being investigated. Genes involved in chromatin regulation are crucial in the biological function of LUAD.

METHODS

The prognostic prediction model for LUAD was developed using multivariables and least absolute shrinkage and selection operator (LASSO) regression. It consisted of 10 chromatin regulators. The LUAD has been divided into two groups, high- and low-risk, using a predictive model. The model was shown to be accurate in predicting survival by the nomogram, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). An analysis of differences in immune-cell infiltration, immunologicalfunction, and clinical traits between low- and high-risk populations was conducted. Protein-protein interaction (PPI) networks and Gene Ontology (GO) pathways of differentially expressed genes (DEGs) in the high versus low risk group were also examined to investigate the association between genes and biological pathways. The biological roles of chromatin regulators (CRs) in LUAD were finally estimated using colony formation and cell movement. The important genes' mRNA expression has been measured using real-time polymerase chain reaction (RT-PCR).

RESULTS AND CONCLUSION

Risk score and stage based on the model could be seen as separate prognostic indicators for patients with LUAD. The main signaling pathway difference across various risk groups was in cell cycle. The immunoinfiltration profile of the tumor microenvironment (TME) and individuals with different risk levels were correlated, suggesting that the interaction of immune cells with the tumor led to the creation of a favorable immunosuppressive microenvironment. These discoveries aid in the creation of individualized therapies for LUAD patients.

Deciphering Brain Metastasis Stem Cell Properties From Colorectal Cancer Highlights Specific Stemness Signature and Shared Molecular Features

BACKGROUND & AIMS

Brain metastases (BMs) from colorectal cancer (CRC) are associated with significant morbidity and mortality, with chemoresistance and short overall survival. Migrating cancer stem cells with the ability to initiate BM have been described in breast and lung cancers. In this study, we describe the identification and characterization of cancer stem cells in BM from CRC.

METHODS

Four brain metastasis stem cell lines from patients with colorectal cancer (BM-SC-CRC1 to BM-SC-CRC4) were obtained by mechanical dissociation of patient's tumors and selection of cancer stem cells by appropriate culture conditions. BM-SC-CRCs were characterized in vitro by clonogenic and limiting-dilution assays, as well as immunofluorescence and Western blot analyses. In ovo, a chicken chorioallantoic membrane (CAM) model and in vivo, xenograft experiments using BALB/c-nude mice were realized. Finally, a whole exome and RNA sequencing analyses were performed.

RESULTS

BM-SC-CRC formed metaspheres and contained tumor-initiating cells with self-renewal properties. They expressed stem cell surface markers (CD44v6, CD44, and EpCAM) in serum-free medium and CRC markers (CK19, CK20 and CDX-2) in fetal bovine serum-enriched medium. The CAM model demonstrated their invasive and migratory capabilities. Moreover, mice intracranial xenotransplantation of BM-SC-CRCs adequately recapitulated the original patient BM phenotype. Finally, transcriptomic and genomic approaches showed a significant enrichment of invasiveness and specific stemness signatures and highlighted KMT2C as a potential candidate gene to potentially identify high-risk CRC patients.

CONCLUSIONS

This original study represents the first step in CRC BM initiation and progression comprehension, and further investigation could open the way to new therapeutics avenues to improve patient prognosis.

Colorectal cancer and therapy response: a focus on the main mechanisms involved

Introduction

The latest GLOBOCAN 2021 reports that colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Most CRC cases are sporadic and associated with several risk factors, including lifestyle habits, gut dysbiosis, chronic inflammation, and oxidative stress.

Aim

To summarize the biology of CRC and discuss current therapeutic interventions designed to counteract CRC development and to overcome chemoresistance.

Methods

Literature searches were conducted using PubMed and focusing the attention on the keywords such as "Current treatment of CRC" or "chemoresistance and CRC" or "oxidative stress and CRC" or "novel drug delivery approaches in cancer" or "immunotherapy in CRC" or "gut microbiota in CRC" or "systematic review and meta-analysis of randomized controlled trials" or "CSCs and CRC". The citations included in the search ranged from September 1988 to December 2022. An additional search was carried out using the clinical trial database.

Results

Rounds of adjuvant therapies, including radiotherapy, chemotherapy, and immunotherapy are commonly planned to reduce cancer recurrence after surgery (stage II and stage III CRC patients) and to improve overall survival (stage IV). 5-fluorouracil-based chemotherapy in combination with other cytotoxic drugs, is the mainstay to treat CRC. However, the onset of the inherent or acquired resistance and the presence of chemoresistant cancer stem cells drastically reduce the efficacy. On the other hand, the genetic-molecular heterogeneity of CRC often precludes also the efficacy of new therapeutic approaches such as immunotherapies. Therefore, the CRC complexity made of natural or acquired multidrug resistance has made it necessary the search for new druggable targets and new delivery systems.

Conclusion

Further knowledge of the underlying CRC mechanisms and a comprehensive overview of current therapeutic opportunities can provide the basis for identifying pharmacological and biological barriers that render therapies ineffective and for identifying new potential biomarkers and therapeutic targets for advanced and aggressive CRC.

Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction

Objective

To identify differences between the composition, abundance, and biological function of the intestinal microbiome of patients with and without lymph-vascular invasion (LVI) colorectal cancer (CRC) and to construct predictive labels to support accurate assessment of LVI in CRC.

Method

134 CRC patients were included, which were divided into two groups according to the presence or absence of LVI, and their intestinal microbiomes were sequenced by 16SrRNA and analyzed for differences. The transcriptome sequencing data of 9 CRC patients were transformed into immune cells abundance matrix by CIBERSORT algorithm, and the correlation among LVI-associated differential intestinal microbiomes, immune cells, immune-related genes and LVI-associated differential GO items and KEGG pathways were analyzed. A random forest (RF) and eXtreme Gradient Boosting (XGB) model were constructed to predict the LVI of CRC patients based on the differential microbiome.

Result

There was no significant difference in α-diversity and β-diversity of intestinal microbiome between CRC patients with and without LVI (P > 0.05). Linear discriminant analysis Effect Size (LEfSe) analysis showed 34 intestinal microbiomes enriched in CRC patients of the LVI group and 5 intestinal microbiomes were significantly enriched in CRC patients of the non-lymph-vascular invasion (NLVI) group. The RF and XGB prediction models constructed with the top 15% of the LVI-associated differential intestinal microbiomes ranked by feature significance had good efficacy.

Conclusions

There are 39 intestinal flora with significantly different species abundance between the LVI and NLVI groups. g:Alistipes.s:Alistipes_indistinctus is closely associated with colorectal cancer vascular invasion. LVI-associated differential intestinal flora may be involved in regulating the infiltration of immune cells in CRC and influencing the expression of immune-related genes. LVI-associated differential intestinal flora may influence the process of vascular invasion in CRC through a number of potential biological functions. RF prediction models and XGB prediction models constructed based on microbial markers of gut flora can be used to predict CRC-LVI conditions.

Crosstalk between Mesenchymal Stem Cells and Cancer Stem Cells Reveals a Novel Stemness-Related Signature to Predict Prognosis and Immunotherapy Responses for Bladder Cancer Patients

Mesenchymal stem cells (MSCs) and cancer stem cells (CSCs) maintain bladder cancer (BCa) stemness and facilitate the progression, metastasis, drug resistance, and prognosis. Therefore, we aimed to decipher the communication networks, develop a stemness-related signature (Stem. Sig.), and identify a potential therapeutic target. BCa single-cell RNA-seq datasets (GSE130001 and GSE146137) were used to identify MSCs and CSCs. Pseudotime analysis was performed by Monocle. Stem. Sig. was developed by analyzing the communication network and gene regulatory network (GRN) that were decoded by NicheNet and SCENIC, respectively. The molecular features of the Stem. Sig. were evaluated in TCGA-BLCA and two PD-(L)1 treated datasets (IMvigor210 and Rose2021UC). A prognostic model was constructed based on a 101 machine-learning framework. Functional assays were performed to evaluate the stem traits of the hub gene. Three subpopulations of MSCs and CSCs were first identified. Based on the communication network, the activated regulons were found by GRN and regarded as the Stem. Sig. Following unsupervised clustering, two molecular subclusters were identified and demonstrated distinct cancer stemness, prognosis, immunological TME, and response to immunotherapy. Two PD-(L)1 treated cohorts further validated the performance of Stem. Sig. in prognosis and immunotherapeutic response prediction. A prognostic model was then developed, and a high-risk score indicated a poor prognosis. Finally, the hub gene SLC2A3 was found exclusively upregulated in extracellular matrix-related CSCs, predicting prognosis, and shaping an immunosuppressive tumor microenvironment. Functional assays uncovered the stem traits of SLC2A3 in BCa by tumorsphere formation and western blotting. The Stem. Sig. derived from MSCs and CSCs can predict prognosis and response to immunotherapy for BCa. Besides, SLC2A3 may serve as a promising stemness target facilitating cancer effective management.

Identification and Validation of Molecular Subtype and Prognostic Signature for Bladder Cancer Based on Neutrophil Extracellular Traps

Neutrophil extracellular traps (NETs) could promote tumor growth and distant metastases. Molecular subtypes of bladder cancer were identified with consensus cluster analysis. A NETs-related prognostic signature was constructed with LASSO cox regression analysis. As a result, we identified three subtypes of bladder cancer, which had a distinct difference in prognosis, immune microenvironment, TIDE score, mRNAsi score and IC50 score. We also developed a prognostic signature based on 5 NETs-related genes, which had a good performance in clinical outcome prediction of bladder cancer. These results may provide more data about the vital role of NETs in bladder cancer.

Old Dog, New Trick: A Tumor-Intrinsic Role for PD-1 in Chemoresistant Tumor Subclones

SUMMARY

Programmed cell death protein 1 (PD-1) is a well-known driver of immunosuppression and lymphocyte-associated disease progression. Increasing evidence suggests a tumor-intrinsic role for PD-1 in promoting chemoresistance via stem-like features. Moving forward, a recent study implies a novel antitumor mechanism for PD-1 inhibition. See related article by Rotolo et al., p. 621.

Cytokine-mediated crosstalk between cancer stem cells and their inflammatory niche from the colorectal precancerous adenoma stage to the cancerous stage: Mechanisms and clinical implications

The majority of colorectal cancers (CRCs) are thought to arise from precancerous adenomas. Upon exposure to diverse microenvironmental factors, precancerous stem cells (pCSCs) undergo complex genetic/molecular changes and gradually progress to form cancer stem cells (CSCs). Accumulative evidence suggests that the pCSC/CSC niche is an inflammatory dominated milieu that contains different cytokines that function as the key communicators between pCSCs/CSCs and their niche and have a decisive role in promoting CRC development, progression, and metastasis. In view of the importance and increasing data about cytokines in modulating pCSCs/CSC stemness properties and their significance in CRC, this review summarizes current new insights of cytokines, such as interleukin (IL)-4, IL-6, IL-8, IL-17A, IL-22, IL-23, IL-33 and interferon (IFN)-γ, involving in the modulation of pCSC/CSC properties and features in precancerous and cancerous lesions and discusses the possible mechanisms of adenoma progression to CRCs and their therapeutic potential.

Tumor-infiltrating immune cells based TMEscore and related gene signature is associated with the survival of CRC patients and response to fluoropyrimidine-based chemotherapy

Background

Tumor-infiltrating immune cells (TIICs) are associated with chemotherapy response. This study aimed to explore the prognostic value of a TIIC-related tumor microenvironment score (TMEscore) in patients with colorectal cancer (CRC) who underwent chemotherapy and construct a TMEscore-related gene signature to determine its predictive value.

Methods

Gene profiles of patients who underwent fluoropyrimidine-based chemotherapy were collected, and their TIIC fractions were calculated and clustered. Differentially expressed genes (DEGs) between clusters were used to calculate the TMEscore. The association between the TMEscore, chemotherapy response, and survival rate was analyzed. Machine learning methods were used to identify key TMEscore-related genes, and a gene signature was constructed to verify the predictive value.

Results

Two clusters based on the TIIC fraction were identified, and the TMEscore was calculated based on the DEGs of the two clusters. The TMEscore was higher in patients who responded to chemotherapy than in those who did not, and was associated with the survival rate of patients who underwent chemotherapy. Three machine learning methods, support vector machine (SVM), decision tree (DT), and Extreme Gradient Boosting (XGBoost), identified three TMEscore-related genes (ADH1C, SLC26A2, and NANS) associated with the response to chemotherapy. A TMEscore-related gene signature was constructed, and three external cohorts validated that the gene signature could predict the response to chemotherapy. Five datasets and clinical samples showed that the expression of the three TMEscore-related genes was increased in tumor tissues compared to those in control tissues.

Conclusions

The TIIC-based TMEscore was associated with the survival of CRC patients who underwent fluoropyrimidine-based chemotherapy, and predicted the response to chemotherapy. The TMEscore-related gene signature had a better predictive value for response to chemotherapy than for survival.