Prognostic analysis of rectal cancer patients after neoadjuvant chemoradiotherapy: different prognostic factors in patients with different TRGs.

PURPOSE: The extent of tumor regression varies widely among locally advanced rectal cancer (LARC) patients who receive neoadjuvant chemoradiotherapy (NCRT) followed by total mesorectal excision (TME). The purpose of this retrospectively study is to assess prognostic factors in LARC patients with NCRT, and further to analyze survival outcomes in patients with different tumor regression grades (TRGs). METHODS: This study includes LARC patients who underwent NCRT and TME at our institution. We retrospectively analyzed the clinicopathological characteristics and survival of all patients, and performed subgroup analysis for patients with different TRGs. Survival differences were compared using the Kaplan-Meier method and the log rank test. Additionally, a multiple Cox proportional hazard model was used to identify independent prognostic factors. RESULTS: The study included 393 patients, with 21.1%, 26.5%, 45.5%, and 6.9% achieving TRG 0, TRG 1, TRG 2, and TRG 3, respectively. The overall survival (OS) rate and disease-free survival (DFS) rate for all patients were 89.4% and 70.7%, respectively. Patients who achieved TRG 0-3 had different 5-year OS rates (96.9%, 91.1%, 85.2%, and 68.8%, P = 0.001) and 5-year DFS rates (80.8%, 72.4%, 67.0%, 55.8%, P = 0.031), respectively. Multivariate analyses showed that the neoadjuvant rectal (NAR) score was an independent prognostic indicator for both overall survival (OS) (HR = 4.040, 95% CI = 1.792-9.111, P = 0.001) and disease-free survival (DFS) (HR = 1.971, 95% CI = 1.478-2.628, P ˂ 0.001). In the subgroup analyses, the NAR score was found to be associated with DFS in patients with TRG 1 and TRG 2. After conducting multivariate analysis, it was found that ypT stage was a significant predictor of DFS for TRG 1 patients (HR = 4.384, 95% CI = 1.721-11.168, P = 0.002). On the other hand, ypN stage was identified as the dominant prognostic indicator of DFS for TRG 2 patients (HR = 2.795, 95% CI = 1.535-5.091, P = 0.001). However, none of these characteristics was found to be correlated with survival in patients with TRG 0 or TRG 3. CONCLUSION: NAR score, in particular, appears to be the most powerful prognostic factor. It is important to consider various prognostic predictors for patients with different TRGs.

Whole transcriptome sequencing analysis reveals the effect of circZFYVE9/miR-378a-3p/IMMT axis on mitochondrial function in adipocytes.

Recent research highlights the complex regulation of lipid accumulation and mitochondrial function in adipocytes via non-coding RNAs like microRNAs and circular non-coding RNAs. Circular non-coding RNAs act as endogenous regulators, impacting lipid metabolism and mitochondrial function by interacting with miRNAs. Sequencing white and brown adipose tissues in mice revealed significant variations in 1936 mRNAs, 127 miRNAs, and 171 circRNAs. Analyses showed these RNAs' involvement in vital processes like mitochondrial biogenesis, oxidative phosphorylation, and the citric acid cycle, crucial for lipid metabolism. Focus on top differentially regulated miRNAs led to the construction of a regulatory network involving circRNAs, miRNAs, and mRNAs, illuminating the role of endogenous RNAs in lipid metabolism and mitochondrial function. The circZFYVE9/miR-378a-3p/IMMT axis was identified as influential in adipogenic differentiation of 3T3-L1 preadipocytes by regulating mitochondrial function. This study expands the understanding of non-coding RNAs in adipose tissue, particularly their connection to mitochondrial function and metabolism.

An immune-related gene prognostic prediction risk model for neoadjuvant chemoradiotherapy in rectal cancer using artificial intelligence.

Background: This study aimed to establish and validate a prognostic model based on immune-related genes (IRGPM) for predicting disease-free survival (DFS) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy, and to elucidate the immune profiles associated with different prognostic outcomes. Methods: Transcriptomic and clinical data were sourced from the Gene Expression Omnibus (GEO) database and the West China Hospital database. We focused on genes from the RNA immune-oncology panel. The elastic net approach was employed to pinpoint immune-related genes significantly impacting DFS. We developed the IRGPM for rectal cancer using the random forest technique. Based on the IRGPM, we calculated prognostic risk scores to categorize patients into high-risk and low-risk groups. Comparative analysis of immune characteristics between these groups was conducted. Results: In this study, 407 LARC samples were analyzed. The elastic net identified a signature of 20 immune-related genes, forming the basis of the IRGPM. Kaplan-Meier survival analysis revealed a lower 5-year DFS in the high-risk group compared to the low-risk group. The receiver operating characteristic (ROC) curve affirmed the model's robust predictive capability. Validation of the model was performed in the GSE190826 cohort and our institution's cohort. Gene expression differences between high-risk and low-risk groups predominantly related to cytokine-cytokine receptor interactions. Notably, the low-risk group exhibited higher immune scores. Further analysis indicated a greater presence of activated B cells, activated CD8 T cells, central memory CD8 T cells, macrophages, T follicular helper cells, and type 2 helper cells in the low-risk group. Additionally, immune checkpoint analysis revealed elevated PDCD1 expression in the low-risk group. Conclusions: The IRGPM, developed through random forest and elastic net methodologies, demonstrates potential in distinguishing DFS among LARC patients receiving standard treatment. Notably, the low-risk group, as defined by the IRGPM, showed enhanced activation of adaptive immune responses within the tumor microenvironment.

Challenges and exploration for immunotherapies targeting cold colorectal cancer.

In recent years, immune checkpoint inhibitors (ICIs) have made significant breakthroughs in the treatment of various tumors, greatly improving clinical efficacy. As the fifth most common antitumor treatment strategy for patients with solid tumors after surgery, chemotherapy, radiotherapy and targeted therapy, the therapeutic response to ICIs largely depends on the number and spatial distribution of effector T cells that can effectively identify and kill tumor cells, features that are also important when distinguishing malignant tumors from "cold tumors" or "hot tumors". At present, only a small proportion of colorectal cancer (CRC) patients with deficient mismatch repair (dMMR) or who are microsatellite instability-high (MSI-H) can benefit from ICI treatments because these patients have the characteristics of a "hot tumor", with a high tumor mutational burden (TMB) and massive immune cell infiltration, making the tumor more easily recognized by the immune system. In contrast, a majority of CRC patients with proficient MMR (pMMR) or who are microsatellite stable (MSS) have a low TMB, lack immune cell infiltration, and have almost no response to immune monotherapy; thus, these tumors are "cold". The greatest challenge today is how to improve the immunotherapy response of "cold tumor" patients. With the development of clinical research, immunotherapies combined with other treatment strategies (such as targeted therapy, chemotherapy, and radiotherapy) have now become potentially effective clinical strategies and research hotspots. Therefore, the question of how to promote the transformation of "cold tumors" to "hot tumors" and break through the bottleneck of immunotherapy for cold tumors in CRC patients urgently requires consideration. Only by developing an in-depth understanding of the immunotherapy mechanisms of cold CRCs can we screen out the immunotherapy-dominant groups and explore the most suitable treatment options for individuals to improve therapeutic efficacy.

Resistance to targeted therapy in metastatic colorectal cancer: Current status and new developments.

Colorectal cancer (CRC) is one of the most lethal and common malignancies in the world. Chemotherapy has been the conventional treatment for metastatic CRC (mCRC) patients. However, the effects of chemotherapy have been unsatisfactory. With the advent of targeted therapy, the survival of patients with CRC have been prolonged. Over the past 20 years, targeted therapy for CRC has achieved substantial progress. However, targeted therapy has the same challenge of drug resistance as chemotherapy. Consequently, exploring the resistance mechanism and finding strategies to address the resistance to targeted therapy, along with searching for novel effective regimens, is a constant challenge in the mCRC treatment, and it is also a hot research topic. In this review, we focus on the current status on resistance to existing targeted therapies in mCRC and discuss future developments.

Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network.

Accurate segmentation of rectal tumors is the most crucial task in determining the stage of rectal cancer and developing suitable therapies. However, complex image backgrounds, irregular edge, and poor contrast hinder the related research. This study presents an attention-based multi-modal fusion module to effectively integrate complementary information from different MRI images and suppress redundancy. In addition, a deep learning-based segmentation model (AF-UNet) is designed to achieve accurate segmentation of rectal tumors. This model takes multi-parametric MRI images as input and effectively integrates the features from different multi-parametric MRI images by embedding the attention fusion module. Finally, three types of MRI images (T2, ADC, DWI) of 250 patients with rectal cancer were collected, with the tumor regions delineated by two oncologists. The experimental results show that the proposed method is superior to the most advanced image segmentation method with a Dice coefficient of [Formula: see text], which is also better than other multi-modal fusion methods. Framework of the AF-UNet. This model takes multi-modal MRI images as input, and integrates complementary information using attention mechanism and suppresses redundancy.

Discovery of X10g as a selective PROTAC degrader of Hsp90α protein for treating breast cancer.

Heat shock protein 90 (Hsp90), a highly conserved and widely expressed molecular chaperone, is mainly responsible for maintaining the correct folding of client proteins and is closely related to the stability and activation of tumour-related proteins. Hsp90α, the major isoform of Hsp90, can promote tumour cell migration and metastasis, and is abundantly secreted in highly invasive tumours. To date, most pan-Hsp90 inhibitors have been limited in their applications due to high toxicity. Herein, we described the candidate compound X10g based on a proteolysis-targeting chimaera (PROTAC) strategy that potently and selectively degraded Hsp90α. The results showed that X10g inhibited tumours better with lower toxicity in vivo. These findings demonstrate that synthesized selective Hsp90α degrader X10g provides a new strategy for breast cancer therapy.

Albumin-encapsulated HSP90-PROTAC BP3 nanoparticles not only retain protein degradation ability but also enhance the antitumour activity of BP3 in vivo.

Proteolysis-targeting chimaera (PROTAC) has received extensive attention in industry. However, there are still some limitations that hinder its further development. In a previous study, our group first demonstrated that the HSP90 degrader BP3 synthesised by the principle of PROTACs showed therapeutic potential for cancer. However, its application was hindered by its high molecular weight and water insolubility. Herein, we aimed to improve these properties of HSP90-PROTAC BP3 by encapsulating it into human serum albumin nanoparticles (BP3@HSA NPs). The results demonstrated that BP3@HSA NPs showed a uniform spherical shape with a size of 141.01 ± 1.07 nm and polydispersity index < 0.2; moreover, BP3@HSA NPs were more readily taken up by breast cancer cells and had a stronger inhibitory effect in vitro than free BP3. BP3@HSA NPs also demonstrated the ability to degrade HSP90. Mechanistically, the improved inhibitory effect of BP3@HSA NPs on breast cancer cells was related to its stronger ability to induce cell cycle arrest and apoptosis. Furthermore, BP3@HSA NPs improved PK properties and showed stronger tumour suppression in mice. Taken together, this study demonstrated that hydrophobic HSP90-PROTAC BP3 nanoparticles encapsulated by human serum albumin could improve the safety and antitumour efficacy of BP3.

Discovery of BP3 as an efficacious proteolysis targeting chimera (PROTAC) degrader of HSP90 for treating breast cancer.

Heat shock protein 90 (HSP90) is involved in the stabilization and activation of oncoproteins, rendering it essential for oncogenic transformation. However, the HSP90 inhibitors evaluated to date have not led to the expected effects in cancer therapy. Herein, we systematically described the design, synthesis, and evaluation of HSP90 degraders based upon the proteolysis-targeting chimera (PROTAC) strategy. The results showed that the candidate compound 16b (BP3) potently degraded HSP90 and effectively inhibited the growth of human breast cancer cells. When used as a single agent, BP3 led to effective tumor suppression in mice. These findings demonstrate that our HSP90-targeting PROTAC strategy has potential novel applications in breast cancer therapy.

A PARP1 PROTAC as a novel strategy against PARP inhibitor resistance via promotion of ferroptosis in p53-positive breast cancer.

Therapeutic targeting of the nuclear enzyme poly (ADP-ribose) polymerase 1 (PARP1) with PARP inhibitors (PARPis) in patients with a homologous recombination (HR)- deficient phenotype based on the mechanism of synthetic lethality has been shown tremendous success in cancer therapy. With the clinical use of various PARPis, emerging evidence has shown that some PARPis offer hope for breakthroughs in triple-negative breast cancer (TNBC) therapy, regardless of HR status. However, similar to other conventional cytotoxic drugs, PARPis are also subject to the intractable problem of drug resistance. Notably, acquired resistance to PARPis caused by point mutations in the PARP1 protein is hard to overcome with current strategies. To explore modalities to overcome resistance and identify patients who are most likely to benefit from PARP1-targeted therapy, we developed a proteolysis-targeted chimaera (PROTAC) to degrade mutant PARP1 in TNBC. Here, we investigated a PARP1 PROTAC termed "NN3″, which triggered ubiquitination and proteasome-mediated degradation of PARP1. Moreover, NN3 degraded PARP1 with resistance-related mutations. Interestingly, compared with other reported PARP1 degraders, NN3 exhibited a unique antitumor mechanism in p53-positive breast cancer cells that effectively promoted ferroptosis by downregulating the SLC7A11 pathway. Furthermore, NN3 showed potent activity and low toxicity in vivo. In conclusion, we propose PROTAC-mediated degradation of PARP1 as a novel strategy against mutation-related PARPi resistance and a paradigm for targeting breast cancer with functional p53 via ferroptosis induction.