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Locally advanced rectal cancer (LARC) presents a clinical challenge due to lack of reliable molecular biomarkers for early diagnosis, prognosis, and prediction of response to neoadjuvant chemoradiotherapy (nCRT). Long non-coding RNAs (lncRNAs) have emerged as promising candidates due to their characteristics and regulatory roles. H19, NEAT1, MALAT1 and HOTAIR are lncRNAs deregulated in gastrointestinal cancers, with insufficient data on their biomarker potential in LARC. The study aimed to analyze the diagnostic, prognostic and predictive utility of H19, NEAT1, MALAT1 and HOTAIR in LARC. Relative expression was evaluated by RT-qPCR in tumor and non-tumor tissues from 25 LARC patients before and after nCRT. H19, NEAT1, and MALAT1 showed significantly altered expression in tumor tissue, as well as non-tumor tissue before and after nCRT. H19 expression was significantly higher in tumor vs. non-tumor tissue before treatment and demonstrated moderate potential to discriminate between tumor and non-tumor. None of the lncRNAs showed statistically significant predictive values for nCRT response or association with treatment outcomes in our study, which was limited by the small number of responders. Our results suggest that H19 might be considered as a potential therapeutic target in LARC. Further studies with larger patient groups are required to confirm its diagnostic and prognostic utility.

Background Transcripts with alternative 5′-untranslated regions (UTRs) result from the activity of alternative promoters and they can determine gene expression by influencing its stability and translational efficiency, thus executing complex regulation of developmental, physiological and pathological processes. Transcriptional regulation of human SMAD4 , a key tumor suppressor deregulated in most gastrointestinal cancers, entails four alternative promoters. These promoters and alternative transcripts they generate remain unexplored as contributors to the SMAD4 deregulation in cancer. The aim of this study was to investigate the relative abundance of the transcript SMAD4–201 in colorectal cell lines and tissues in order to establish if its fluctuations may be associated with colorectal cancer (CRC). Methods Relative abundance of SMAD4–201 in total SMAD4 mRNA was analyzed using quantitative PCR in a set of permanent human colon cell lines and tumor and corresponding healthy tissue samples from patients with CRC. Results The relative abundance of SMAD4–201 in analyzed cell lines varied between 16 and 47%. A similar relative abundance of SMAD4–201 transcript was found in the majority of analyzed human tumor tissue samples, and it was averagely 20% lower in non-malignant in comparison to malignant tissue samples ( p  = 0.001). Transcript SMAD4–202 was not detectable in any of the analyzed samples, so the observed fluctuations in the composition of SMAD4 transcripts can be attributed to transcripts other than SMAD4–201 and SMAD4–202. Conclusion The expression profile of SMAD4–201 in human tumor and non-tumor tissue samples may indicate the translational potential of this molecule in CRC, but further research is needed to clarify its usability as a potential biomarker for early diagnosis.

Background/Objectives: With advances in surgical techniques for rectal cancer—particularly laparoscopic and robotic-assisted approaches—the choice of operative method may influence not only oncological but also functional outcomes. This study aimed to compare open and laparoscopic rectal cancer surgery regarding pelvic autonomic nerve identification, preservation and its impact on postoperative urinary, sexual, and quality-of-life outcomes. Methods: A total of 181 patients who underwent curative rectal cancer surgery at the Clinic for Digestive Surgery, University Clinical Center of Serbia, were included. Six types of procedures were performed using both open and laparoscopic approaches. Intraoperative identification and preservation of pelvic autonomic nerves were assessed and verified postoperatively through evaluation of urinary, sexual, and quality-of-life parameters. Urinary function and related life quality were assessed using the International Prostate Symptom Score (IPSS and IPSS-QoL), while sexual function was evaluated using gender-specific validated questionnaires (IIEF-15 and FSFI) preoperatively and at 2, 4, and 6 months postoperatively. Results: Nerve non-visualization and/or injuries were significantly more frequent in the open surgery group. The laparoscopic approach was associated with better preservation of urinary function, particularly among male patients, better sexual function in both sexes, and a transient advantage in quality of life. Conclusions: Laparoscopic rectal cancer surgery demonstrated superior pelvic autonomic nerve visualization and preservation and better short-term urinary and sexual function. Nonetheless, quality-of-life outcomes were comparable by 6 months of follow-up, underscoring the importance of meticulous nerve-preserving technique regardless of surgical approach.

Reliable and accurate prediction of treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) is usually demanding and continues to pose a challenge. Kurtosis as a histogram parameter calculated on T2-weighted MRI sequences might be an additional tool, as it represents a quantitative biomarker for response prediction. It is defined as a measure of distributions' tails relative to the center of the distribution curve, which reflects tissue heterogeneity. The aim of the study was to evaluate the added value of T2-weighted kurtosis in predicting pathological response to nCRT in patients with LARC. a single-center cohort study included 71 patients with LARC who underwent both initial and post-nCRT MRI examinations followed by surgical resection in the form of the total mesorectal excision (TME). Histogram analysis was performed using software MIPAV (Medical Image Processing, Analysis, and Visualization, version 11.3.2, developed by the National Institutes of Health, Bethesda, MD, USA) on T2-weighted sequences, extracting kurtosis along with other histogram parameters. Pathological tumor regression grade (pTRG) in accordance with Mandard classification was considered the gold standard. Patients were classified as responders (pTRG 1-2) or non-responders (pTRG 3-5). while other histogram parameters did not show statistically significant differences between groups, post-treatment values of kurtosis were significantly higher in responders compared to non-responders (4.28 ± 0.73 vs. 3.01 ± 0.17, = 0.024). The F1 score as a classification metric (0.821) indicates an improvement in classification performance following therapy. T2-weighted kurtosis might be a significant tool in predicting pathological response to nCRT, representing a potentially valuable quantitative biomarker that could improve treatment response assessment.

Quantitative systems pharmacology (QSP) modeling has become increasingly important in pharmaceutical research and development, and is a powerful tool to gain mechanistic insights into the complex dynamics of biological systems in response to drug treatment. However, even once a suitable mathematical framework to describe the pathophysiology and mechanisms of interest is established, final model calibration and the exploration of variability can be challenging and time consuming. QSP models are often formulated as multi-scale, multi-compartment nonlinear systems of ordinary differential equations. Commonly accepted modeling strategies, workflows, and tools have promise to greatly improve the efficiency of QSP methods and improve productivity. In this paper, we present the QSP Toolbox, a set of functions, structure array conventions, and class definitions that computationally implement critical elements of QSP workflows including data integration, model calibration, and variability exploration. We present the application of the toolbox to an ordinary differential equations-based model for antibody drug conjugates. As opposed to a single stepwise reference model calibration, the toolbox also facilitates simultaneous parameter optimization and variation across multiple in vitro, in vivo, and clinical assays to more comprehensively generate alternate mechanistic hypotheses that are in quantitative agreement with available data. The toolbox also includes scripts for developing and applying virtual populations to mechanistic exploration of biomarkers and efficacy. We anticipate that the QSP Toolbox will be a useful resource that will facilitate implementation, evaluation, and sharing of new methodologies in a common framework that will greatly benefit the community.

Incidence rates of colorectal cancer vary geographically and have changed over time 1 . Notably, in the past two decades, the incidence of early-onset colorectal cancer, which affects individuals below 50 years of age, has doubled in many countries 2 , 3 , 4 – 5 . The reasons for this increase are unknown. Here we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite-unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown aetiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin 6 , 7 , had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals who were diagnosed before 40 years of age than in those over 70 years of age, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked to APC driver mutations, with ID18 being responsible for about 25% of APC driver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that mutagenic exposure to colibactin-producing bacteria in early life may contribute to the increasing incidence of early-onset colorectal cancer. Analysis of genomic data from 981 colorectal cancers from participants in 11 countries reveals variations in mutational signatures of microsatellite-stable cancers that are dependent on geographical origin and age at which the cancer was diagnosed.

The Schlafen 11 (SLFN11) protein has recently emerged as pivotal in DNA damage conditions, with predictive potential for tumor response to cytotoxic chemotherapies. Recent discoveries also showed that the programmed death ligand 1 (PD-L1) protein can be found on malignant cells, providing an immune evasion mechanism exploited by different tumors. Additionally, excessive generation of free radicals, redox imbalance, and consequential DNA damage can affect intestinal cell homeostasis and lead to neoplastic transformation. Therefore, our study aimed to investigate the significance of SLFN11 and PD-L1 proteins and redox status parameters as prognostic biomarkers in CRC patients. This study included a total of 155 CRC patients. SLFN11 and PD-L1 serum levels were measured with ELISA and evaluated based on redox status parameters, sociodemographic and clinical characteristics, and survival. The following redox status parameters were investigated: spectrophotometrically measured superoxide dismutase (SOD), sulfhydryl (SH) groups, advanced oxidation protein products (AOPP), malondialdehyde (MDA), pro-oxidant–antioxidant balance (PAB), and superoxide anion (O2•–). The prooxidative score, antioxidative score, and OXY-SCORE were also calculated. The results showed significantly shorter survival in patients with higher OXY-SCOREs and higher levels of serum SLFN11, while only histopathology-analysis-related factors showed significant prognostic value. OXY-SCORE and SLFN11 levels may harbor prognostic potential in CRC patients.

Multiple software programs are available for designing and running large scale system-level pharmacology models used in the drug development process. Depending on the problem, scientists may be forced to use several modeling tools that could increase model development time, IT costs and so on. Therefore, it is desirable to have a single platform that allows setting up and running large-scale simulations for the models that have been developed with different modeling tools. We developed a workflow and a software platform in which a model file is compiled into a self-contained executable that is no longer dependent on the software that was used to create the model. At the same time the full model specifics is preserved by presenting all model parameters as input parameters for the executable. This platform was implemented as a model agnostic, therapeutic area agnostic and web-based application with a database back-end that can be used to configure, manage and execute large-scale simulations for multiple models by multiple users. The user interface is designed to be easily configurable to reflect the specifics of the model and the user's particular needs and the back-end database has been implemented to store and manage all aspects of the systems, such as Models, Virtual Patients, User Interface Settings, and Results. The platform can be adapted and deployed on an existing cluster or cloud computing environment. Its use was demonstrated with a metabolic disease systems pharmacology model that simulates the effects of two antidiabetic drugs, metformin and fasiglifam, in type 2 diabetes mellitus patients.

Multiple software programs are available for designing and running large scale system-level pharmacology models used in the drug development process. Depending on the problem, scientists may be forced to use several modeling tools that could increase model development time, IT costs and so on. Therefore, it is desirable to have a single platform that allows setting up and running large-scale simulations for the models that have been developed with different modeling tools. We developed a workflow and a software platform in which a model file is compiled into a self-contained executable that is no longer dependent on the software that was used to create the model. At the same time the full model specifics is preserved by presenting all model parameters as input parameters for the executable. This platform was implemented as a model agnostic, therapeutic area agnostic and web-based application with a database back-end that can be used to configure, manage and execute large-scale simulations for multiple models by multiple users. The user interface is designed to be easily configurable to reflect the specifics of the model and the user's particular needs and the back-end database has been implemented to store and manage all aspects of the systems, such as Models, Virtual Patients, User Interface Settings, and Results. The platform can be adapted and deployed on an existing cluster or cloud computing environment. Its use was demonstrated with a metabolic disease systems pharmacology model that simulates the effects of two antidiabetic drugs, metformin and fasiglifam, in type 2 diabetes mellitus patients.